Model

class astropy.modeling.Model

Bases: object

Base class for all models.

This is an abstract class and should not be instantiated directly.

This class sets the constraints and other properties for all individual parameters and performs parameter validation.

Parameters:

name : str, optional

A human-friendly name associated with this model instance (particularly useful for identifying the individual components of a compound model).

fixed : dict, optional

Dictionary {parameter_name: bool} setting the fixed constraint for one or more parameters. True means the parameter is held fixed during fitting and is prevented from updates once an instance of the model has been created.

Alternatively the fixed property of a parameter may be used to lock or unlock individual parameters.

tied : dict, optional

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 to set the tied constraint on individual parameters.

bounds : dict, optional

Dictionary {parameter_name: value} 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 or ~astropy.modeling.Parameter.bounds` properties of a parameter may be used to set bounds on individual parameters.

eqcons : list, optional

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

ineqcons : list, optional

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

Examples

>>> from astropy.modeling import models
>>> def tie_center(model):
...         mean = 50 * model.stddev
...         return mean
>>> tied_parameters = {'mean': tie_center}

Specify that 'mean' is a tied parameter in one of two ways:

>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
...                        tied=tied_parameters)

or

>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.mean.tied
False
>>> g1.mean.tied = tie_center
>>> g1.mean.tied
<function tie_center at 0x...>

Fixed parameters:

>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3,
...                        fixed={'stddev': True})
>>> g1.stddev.fixed
True

or

>>> g1 = models.Gaussian1D(amplitude=10, mean=5, stddev=.3)
>>> g1.stddev.fixed
False
>>> g1.stddev.fixed = True
>>> g1.stddev.fixed
True

Attributes Summary

bounds	A dict mapping parameter names to their upper and lower bounds as (min, max) tuples.
eqcons	List of parameter equality constraints.
fittable
fixed	A dict mapping parameter names to their fixed constraint.
ineqcons	List of parameter inequality constraints.
inputs	The name(s) of the input variable(s) on which a model is evaluated.
inverse	Returns a new Model instance which performs the inverse transform, if an analytic inverse is defined for this model.
linear
model_constraints	Primarily for informational purposes, these are the types of constraints that constrain model evaluation.
model_set_axis	The index of the model set axis–that is the axis of a parameter array that pertains to which model a parameter value pertains to–as specified when the model was initialized.
n_inputs	The number of inputs to this model.
n_outputs	The number of outputs from this model.
name	User-provided name for this model instance.
outputs	The name(s) of the output(s) of the model.
param_dim	Deprecated since version 0.4.
param_names	Names of the parameters that describe models of this type.
param_sets	Return parameters as a pset.
parameter_constraints	Primarily for informational purposes, these are the types of constraints that can be set on a model’s parameters.
parameters	A flattened array of all parameter values in all parameter sets.
standard_broadcasting
tied	A dict mapping parameter names to their tied constraint.

Methods Summary

__call__(*inputs, **kwargs)	Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.
add_model(*args, **kwargs)	Deprecated since version 1.0.
copy()	Return a copy of this model.
evaluate(*args, **kwargs)	Evaluate the model on some input variables.
prepare_inputs(*inputs, **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.
prepare_outputs(format_info, *outputs, **kwargs)
rename(name)	Creates a copy of this model class with a new name.

Attributes Documentation

bounds

A dict mapping parameter names to their upper and lower bounds as (min, max) tuples.

eqcons

List of parameter equality constraints.

fittable = False

fixed

A dict mapping parameter names to their fixed constraint.

ineqcons

List of parameter inequality constraints.

inputs = ()

The name(s) of the input variable(s) on which a model is evaluated.

inverse

Returns a new Model instance which performs the inverse transform, if an analytic inverse is defined for this model.

Even on models that don’t have an inverse defined, this property can be set with a manually-defined inverse, such a pre-computed or experimentally determined inverse (often given as a PolynomialModel, but not by requirement).

Note to authors of Model subclasses: To define an inverse for a model simply override this property to return the appropriate model representing the inverse. The machinery that will make the inverse manually-overridable is added automatically by the base class.

linear = True

model_constraints = (u'eqcons', u'ineqcons')

Primarily for informational purposes, these are the types of constraints that constrain model evaluation.

model_set_axis

The index of the model set axis–that is the axis of a parameter array that pertains to which model a parameter value pertains to–as specified when the model was initialized.

See the documentation on Model Sets for more details.

n_inputs

The number of inputs to this model.

Equivalent to len(model.inputs).

n_outputs

The number of outputs from this model.

Equivalent to len(model.outputs).

name

User-provided name for this model instance.

outputs = ()

The name(s) of the output(s) of the model.

param_dim

Deprecated since version 0.4: The param_dim function is deprecated and may be removed in a future version. Use len(model) instead.

param_names = ()

Names of the parameters that describe models of this type.

The parameters in this tuple are in the same order they should be passed in when initializing a model of a specific type. Some types of models, such as polynomial models, have a different number of parameters depending on some other property of the model, such as the degree.

When defining a custom model class the value of this attribute is automatically set by the Parameter attributes defined in the class body.

param_sets

Return parameters as a pset.

This is a list with one item per parameter set, which is an array of that parameter’s values across all parameter sets, with the last axis associated with the parameter set.

parameter_constraints = (u'fixed', u'tied', u'bounds')

Primarily for informational purposes, these are the types of constraints that can be set on a model’s parameters.

parameters

A flattened array of all parameter values in all parameter sets.

Fittable parameters maintain this list and fitters modify it.

standard_broadcasting = True

tied

A dict mapping parameter names to their tied constraint.

Methods Documentation

__call__(*inputs, **kwargs)

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

add_model(*args, **kwargs)

Deprecated since version 1.0: The add_model function is deprecated and may be removed in a future version. Use Use Model operators (TODO: link to compound model docs instead.

Create a CompositeModel by chaining the current model with the new one using the specified mode.

Parameters:

model : an instance of a subclass of Model

mode : string

‘parallel’, ‘serial’, ‘p’ or ‘s’ a flag indicating whether to combine the models in series or in parallel

Returns:

model : CompositeModel

an instance of CompositeModel

copy()

Return a copy of this model.

Uses a deep copy so that all model attributes, including parameter values, are copied as well.

evaluate(*args, **kwargs)

Evaluate the model on some input variables.

prepare_inputs(*inputs, **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.

prepare_outputs(format_info, *outputs, **kwargs)

classmethod rename(name)

Creates a copy of this model class with a new name.

The new class is technically a subclass of the original class, so that instance and type checks will still work. For example:

>>> from astropy.modeling.models import Rotation2D
>>> SkyRotation = Rotation2D.rename('SkyRotation')
>>> SkyRotation
<class '__main__.SkyRotation'>
Name: SkyRotation (Rotation2D)
Inputs: ('x', 'y')
Outputs: ('x', 'y')
Fittable parameters: ('angle',)
>>> issubclass(SkyRotation, Rotation2D)
True
>>> r = SkyRotation(90)
>>> isinstance(r, Rotation2D)
True