Model¶
-
class
astropy.modeling.
Model
[source] [edit on github]¶ 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.
The following initialization arguments apply to the majority of Model subclasses by default (exceptions include specialized utility models like
Mapping
). Parametric models take all their parameters as arguments, followed by any of the following optional keyword arguments:Parameters: name : str, optional
A human-friendly name associated with this model instance (particularly useful for identifying the individual components of a compound model).
meta : dict, optional
An optional dict of user-defined metadata to attach to this model. How this is used and interpreted is up to the user or individual use case.
n_models : int, optional
If given an integer greater than 1, a model set is instantiated instead of a single model. This affects how the parameter arguments are interpreted. In this case each parameter must be given as a list or array–elements of this array are taken along the first axis (or
model_set_axis
if specified), such that the Nth element is the value of that parameter for the Nth model in the set.See the section on model sets in the documentation for more details.
model_set_axis : int, optional
This argument only applies when creating a model set (i.e.
n_models > 1
). It changes how parameter values are interpreted. Normally the first axis of each input parameter array (properly the 0th axis) is taken as the axis corresponding to the model sets. However, any axis of an input array may be taken as this “model set axis”. This accepts negative integers as well–for example usemodel_set_axis=-1
if the last (most rapidly changing) axis should be associated with the model sets.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 thetied
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
andmax
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
Attributes Documentation
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bounds
¶ A
dict
mapping parameter names to their upper and lower bounds as(min, max)
tuples.
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eqcons
¶ List of parameter equality constraints.
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fittable
= False¶
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fixed
¶ A
dict
mapping parameter names to their fixed constraint.
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ineqcons
¶ List of parameter inequality constraints.
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inputs
= ()¶ The name(s) of the input variable(s) on which a model is evaluated.
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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.
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linear
= True¶
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model_constraints
= (u'eqcons', u'ineqcons')¶ Primarily for informational purposes, these are the types of constraints that constrain model evaluation.
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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.
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n_inputs
¶ The number of inputs to this model.
Equivalent to
len(model.inputs)
.
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n_outputs
¶ The number of outputs from this model.
Equivalent to
len(model.outputs)
.
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name
¶ User-provided name for this model instance.
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outputs
= ()¶ The name(s) of the output(s) of the model.
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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.
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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.
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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.
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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.
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parameters
¶ A flattened array of all parameter values in all parameter sets.
Fittable parameters maintain this list and fitters modify it.
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standard_broadcasting
= True¶
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tied
¶ A
dict
mapping parameter names to their tied constraint.
Methods Documentation
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__call__
(*inputs, **kwargs)[source] [edit on github]¶ Evaluate this model using the given input(s) and the parameter values that were specified when the model was instantiated.
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add_model
(*args, **kwargs)[source] [edit on github]¶ 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
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copy
()[source] [edit on github]¶ Return a copy of this model.
Uses a deep copy so that all model attributes, including parameter values, are copied as well.
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evaluate
(*args, **kwargs)[source] [edit on github]¶ Evaluate the model on some input variables.
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prepare_inputs
(*inputs, **kwargs)[source] [edit on github]¶ 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.
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prepare_outputs
(format_info, *outputs, **kwargs)[source] [edit on github]¶
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classmethod
rename
(name)[source] [edit on github]¶ 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
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