Polynomial1D

class astropy.modeling.polynomial.Polynomial1D(degree, domain=None, window=None, n_models=None, model_set_axis=None, name=None, meta=None, **params)

Bases: _PolyDomainWindow1D

1D Polynomial model.

It is defined as:

\[P = \sum_{i=0}^{i=n}C_{i} * x^{i}\]

For explanation of domain, and window see Notes regarding usage of domain and window.

Parameters:

degreepython:int

degree of the series

domainpython:tuple or python:None, optional

If None, it is set to (-1, 1)

windowpython:tuple or python:None, optional

If None, it is set to (-1, 1) Fitters will remap the domain to this window

**paramspython:dict

keyword: value pairs, representing parameter_name: value

Other Parameters:

fixeda python:dict, optional

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.

tiedpython:dict, optional

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.

boundspython:dict, optional

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

eqconspython:list, optional

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

ineqconspython:list, optional

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

Attributes Summary

input_units	This property is used to indicate what units or sets of units the evaluate method expects, and returns a dictionary mapping inputs to units (or None if any units are accepted).
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, *coeffs)	Evaluate the model on some input variables.
fit_deriv(x, *params)	Computes the Vandermonde matrix.
horner(x, coeffs)
prepare_inputs(x, **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

input_units

n_inputs = 1

The number of inputs.

n_outputs = 1

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, *coeffs)

Evaluate the model on some input variables.

fit_deriv(x, *params)

Computes the Vandermonde matrix.

Parameters:

xndarray

input

*params

throw-away parameter list returned by non-linear fitters

Returns:

resultndarray

The Vandermonde matrix

static horner(x, coeffs)

prepare_inputs(x, **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. This also makes sure that (if applicable) the units of the input will be compatible with the evaluate method.