CompoundModel#

class astropy.modeling.CompoundModel(op, left, right, name=None)[source]#

Bases: Model

Base class for compound models.

While it can be used directly, the recommended way to combine models is through the model operators.

Attributes Summary

`eqcons`	List of parameter equality constraints.
`fittable`	Set the fittable attribute on a compound model.
`has_user_bounding_box`	A flag indicating whether or not a custom bounding_box has been assigned to this model by a user, via assignment to `model.bounding_box`.
`ineqcons`	List of parameter inequality constraints.
`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).
`input_units_allow_dimensionless`	Allow dimensionless input (and corresponding output).
`input_units_equivalencies`
`input_units_strict`	Enforce strict units on inputs to evaluate.
`isleaf`
`n_inputs`	The number of inputs of a model.
`n_outputs`	The number of outputs of a model.
`n_submodels`	Return the number of components in a single model, which is obviously 1.
`param_names`	An ordered list of parameter names.
`return_units`	This property is used to indicate what units or sets of units the output of evaluate should be in, and returns a dictionary mapping outputs to units (or `None` if any units are accepted).
`submodel_names`	Return the names of submodels in a `CompoundModel`.

Methods Summary

`evaluate`(args, *kw)	Evaluate the model on some input variables.
`inputs_map`()	Map the names of the inputs to this ExpressionTree to the inputs to the leaf models.
`outputs_map`()	Map the names of the outputs to this ExpressionTree to the outputs to the leaf models.
`rename`(name)	Creates a copy of this model class with a new name, inputs or outputs.
`render`([out, coords])	Evaluate a model at fixed positions, respecting the `bounding_box`.
`replace_submodel`(name, model)	Construct a new `CompoundModel` instance from an existing CompoundModel, replacing the named submodel with a new model.
`traverse_postorder`([include_operator])	Postorder traversal of the CompoundModel tree.
`with_units_from_data`(**kwargs)	See `with_units_from_data` for overview of this method.
`without_units_for_data`(**kwargs)	See `without_units_for_data` for overview of this method.

Attributes Documentation

eqcons#

fittable#: Set the fittable attribute on a compound model.

has_user_bounding_box#: A flag indicating whether or not a custom bounding_box has been assigned to this model by a user, via assignment to model.bounding_box.

ineqcons#

input_units#

input_units_allow_dimensionless#

input_units_equivalencies#

input_units_strict#

isleaf#

n_inputs#: The number of inputs.

n_outputs#: The number of outputs.

n_submodels#

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.

return_units#

submodel_names#: Return the names of submodels in a CompoundModel.

Methods Documentation

evaluate(*args, **kw)[source]#: Evaluate the model on some input variables.

inputs_map()[source]#: Map the names of the inputs to this ExpressionTree to the inputs to the leaf models.

outputs_map()[source]#: Map the names of the outputs to this ExpressionTree to the outputs to the leaf models.

rename(name)[source]#

Creates a copy of this model class with a new name, inputs or outputs.

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 'astropy.modeling.core.SkyRotation'>
Name: SkyRotation (Rotation2D)
N_inputs: 2
N_outputs: 2
Fittable parameters: ('angle',)
>>> issubclass(SkyRotation, Rotation2D)
True
>>> r = SkyRotation(90)
>>> isinstance(r, Rotation2D)
True

render(out=None, coords=None)[source]#

Evaluate a model at fixed positions, respecting the bounding_box.

The key difference relative to evaluating the model directly is that this method is limited to a bounding box if the Model.bounding_box attribute is set.

Parameters:

outnumpy.ndarray, optional: An array that the evaluated model will be added to. If this is not given (or given as None), a new array will be created.
coordsarray_like, optional: An array to be used to translate from the model’s input coordinates to the out array. It should have the property that self(coords) yields the same shape as out. If out is not specified, coords will be used to determine the shape of the returned array. If this is not provided (or None), the model will be evaluated on a grid determined by Model.bounding_box.

Returns:

outnumpy.ndarray: The model added to out if out is not None, or else a new array from evaluating the model over coords. If out and coords are both None, the returned array is limited to the Model.bounding_box limits. If Model.bounding_box is None, arr or coords must be passed.

Raises:

ValueError: If coords are not given and the Model.bounding_box of this model is not set.

Examples

Efficient Model Rendering with Bounding Boxes

replace_submodel(name, model)[source]#

Construct a new CompoundModel instance from an existing CompoundModel, replacing the named submodel with a new model.

In order to ensure that inverses and names are kept/reconstructed, it’s necessary to rebuild the CompoundModel from the replaced node all the way back to the base. The original CompoundModel is left untouched.

Parameters:

namestr: name of submodel to be replaced
modelModel: replacement model

traverse_postorder(include_operator=False)[source]#: Postorder traversal of the CompoundModel tree.

with_units_from_data(**kwargs)[source]#

See with_units_from_data for overview of this method.

Notes

This modifies the behavior of the base method to account for the case where the sub-models of a compound model have different output units. This is only valid for compound * and / compound models as in that case it is reasonable to mix the output units. In order to do this it requires some additional information output by without_units_for_data passed as keyword arguments under the keywords _left_kwargs and _right_kwargs.

Outside the mixed output units, this method is identical to the base method.

without_units_for_data(**kwargs)[source]#

See without_units_for_data for overview of this method.

Notes

This modifies the behavior of the base method to account for the case where the sub-models of a compound model have different output units. This is only valid for compound * and / compound models as in that case it is reasonable to mix the output units. It does this by modifying the output units of each sub model by using the output units of the other sub model so that we can apply the original function and get the desired result.

Additional data has to be output in the mixed output unit case so that the units can be properly rebuilt by with_units_from_data.

Outside the mixed output units, this method is identical to the base method.