autofit.Model#

class autofit.Model(cls, **kwargs)[source]#

Bases: AbstractPriorModel

@DynamicAttrs

The object a Python class is input into to create a model-component, which has free parameters that are fitted by a non-linear search.

The Model object is flexible, and can create models from many input Python data structures (e.g. a list of classes, dictionary of classes, hierarchy of classes).

For a complete description of the model composition API, see the PyAutoFit model API cookbooks:

https://pyautofit.readthedocs.io/en/latest/cookbooks/cookbook_1_basics.html

The Python class input into a Model to create a model component is written using the following format:

The name of the class is the name of the model component (e.g. Gaussian).
The input arguments of the constructor are the parameters of the mode (e.g. centre, normalization and sigma).
The default values of the input arguments tell PyAutoFit whether a parameter is a single-valued float or a

multi-valued tuple.

[Rich explain everything else]

Parameters:: cls – The class associated with this instance

Examples

class Gaussian:

def __init__(
self, centre=0.0, # <- PyAutoFit recognises these normalization=0.1, # <- constructor arguments are sigma=0.01, # <- the Gaussian’s parameters.

):
self.centre = centre self.normalization = normalization self.sigma = sigma

model = af.Model(Gaussian)

Methods

`add_assertion`	Assert that some relationship holds between physical values associated with priors at the point an instance is created.
`all_paths_for_prior`	Find all paths that points at the given tuple.
`as_model`
`attribute_tuples_with_type`	Tuples describing the name and instance for attributes in the model with a given type, recursively.
`cast`	Cast models to a new type.
`check_assertions`	Check that all assertions are satisfied by the given arguments.
`copy`	Create a copy of the model.
`copy_with_fixed_priors`	Recursively overwrite priors in the mapper with instance values from the instance except where the containing class is the descendant of a listed class.
`dict`	A dictionary representation of this object
`direct_tuples_with_type`
`freeze`	Freeze this object.
`from_dict`	Recursively parse a dictionary returning the model, collection or instance that is represents.
`from_instance`	Recursively create a prior object model from an object model.
`from_json`	Loads the model from a .json file, which was written using the model's dictionary (dict) attribute as follows:
`from_object`	Convert an arbitrary object into an appropriate prior model representation.
`gaussian_prior_model_for_arguments`	Returns a new instance of model mapper with a set of Gaussian priors based on tuples provided by a previous nonlinear search.
`has`
`has_instance`	True iff this model contains an instance of type cls, recursively.
`has_model`	True iff this model contains a Model of type cls, recursively.
`index`	Retrieve the index of a given path in the model
`instance_flatten`	Flatten an instance of this model as a PyTree.
`instance_for_arguments`	Returns an instance of the model for a set of arguments
`instance_from_path_arguments`	Create an instance from a dictionary mapping paths to tuples to corresponding values.
`instance_from_prior_medians`	Returns a list of physical values from the median values of the priors. :returns: If True, the assertions attached to this model (e.g. that one parameter > another parameter) are ignored and not checked. :rtype: ignore_assertions.
`instance_from_prior_name_arguments`	Instantiate the model from the names of priors and corresponding values.
`instance_from_unit_vector`	Returns a ModelInstance, which has an attribute and class instance corresponding to every Model attributed to this instance.
`instance_from_vector`	Returns a ModelInstance, which has an attribute and class instance corresponding to every Model attributed to this instance.
`instance_unflatten`	Unflatten a PyTree into an instance of this model.
`is_only_model`	True iff this model contains at least one Model of type cls and contains no PriorModels that are not of type cls, recursively.
`items`	Return (name, value) pairs for all public, non-internal attributes.
`log_prior_list_from`	Compute the total log prior for each parameter vector in a list.
`log_prior_list_from_vector`	Compute the log priors of every parameter in a vector, using the Prior of every parameter.
`make_prior`	Returns a prior for an attribute of a class with a given name.
`make_tuple_prior`
`mapper_from_partial_prior_arguments`	Returns a new model mapper from a dictionary mapping existing priors to new priors, keeping existing priors where no mapping is provided.
`mapper_from_prior_arguments`	Returns a new model mapper from a dictionary mapping existing priors to new priors.
`mapper_from_prior_means`	The widths of the new priors are taken from the width_config. The new gaussian priors must be provided in the same order as the priors associated with model. If a is not None then all priors are created with an absolute width of a. If r is not None then all priors are created with a relative width of r. :param means: The median PDF value of every Gaussian, which centres each GaussianPrior. :param r: The relative width to be assigned to gaussian priors :param a: print(tuples[i][1], width) The absolute width to be assigned to gaussian priors :param tuples: A list of tuples each containing the mean and width of a prior.
`mapper_from_uniform_floats`	The widths of the new priors are the floats value minus and plus the input bound b.
`mapper_via_defaults_from`	The widths of the new priors are taken from the width_config. The new gaussian priors must be provided in the same order as the priors associated with model. If a is not None then all priors are created with an absolute width of a. If r is not None then all priors are created with a relative width of r. :param means: The median PDF value of every Gaussian, which centres each GaussianPrior. :param r: The relative width to be assigned to gaussian priors :param a: print(tuples[i][1], width) The absolute width to be assigned to gaussian priors :param tuples: A list of tuples each containing the mean and width of a prior.
`model_tuples_with_type`	All models of the class in this model which have at least one free parameter, recursively.
`models_with_type`	Return all models of a given type in the model tree.
`name_for_prior`	Get the underscore-separated name for a prior in this model.
`next_id`
`object_for_path`	Get the object at a given path.
`path_for_name`	Find the path to a prior in the model that matches a given name.
`path_for_object`	Find a path that points at the given object.
`path_for_prior`	Find a path that points at the given tuple.
`path_instance_tuples_for_class`	Tuples containing the path tuple and instance for every instance of the class in the model tree.
`prior_with_id`	Retrieve a prior by its unique integer id.
`product`	Combine multiple models with the same structure by replacing priors with priors that contain a message which is the product of the messages of the priors with the same path in each model.
`random_instance`	Returns a random instance of the model.
`random_instance_from_priors_within_limits`	Returns a random instance of physical values by drawing uniform random values between lower and upper limits defined as unit values, using the model priors to map them from unit values to physical values.
`random_unit_vector_within_limits`	Generate a random vector of unit values by drawing uniform random values between 0 and 1.
`random_vector_from_priors_within_limits`	Returns a random vector of physical values by drawing uniform random values between lower and upper limits defined as unit values, using the model priors to map them from unit values to physical values.
`replacing`	Return a new model with some priors replaced.
`replacing_for_path`	Create a new model replacing the value for a given path with a new value
`set_item_at_path`	Set an item at a path in the model.
`sort_priors_alphabetically`	Sort priors by their paths according to this model.
`take_attributes`	Take all attributes with a matching path from the source prior model.
`tree_flatten`	Flatten this model as a PyTree.
`tree_unflatten`	Unflatten a PyTree into a model.
`unfreeze`	Unfreeze this object.
`vector_from_unit_vector`
`with_limits`	Create a new instance of this model where each prior is updated to lie between new limits.
`with_paths`	Recursively generate a copy of this model retaining only objects specified by the list of paths.
`without_attributes`	Returns a copy of this object with all priors, prior models and constants removed.
`without_paths`	Recursively generate a copy of this model retaining only objects not specified by the list of paths.

Attributes

`all_name_prior_tuples`	Maps a tuple containing all names for a given prior to that prior.
`all_names`	All possible names for all priors grouped such that all names for a given prior are collected together in a tuple.
`all_paths`	All possible paths to all priors grouped such that all paths to the same prior are collected together in a tuple.
`all_paths_prior_tuples`	Maps a tuple containing all paths to a given prior to that prior.
`assertions`	The list of assertion constraints attached to this model.
`component_number`
`composition`	A list of dot-separated path strings for each prior, ordered by prior id.
`constructor_argument_names`	The argument names of the constructor of the class of this model.
`direct_argument_names`	The names of priors, constants and other attributes that are direct attributes of this model.
`direct_deferred_tuples`	(name, deferred_argument) tuples for direct DeferredArgument attributes.
`direct_instance_tuples`	(name, value) tuples for direct float and Constant attributes.
`direct_prior_model_tuples`	(name, prior_model) tuples for immediate child prior models (non-recursive).
`direct_prior_tuples`	returns: direct_priors :rtype: [(String, Prior)]
`direct_tuple_priors`	(name, tuple_prior) tuples for direct TuplePrior attributes.
`identifier`
`info`	Use the priors that make up the model_mapper to generate information on each parameter of the overall model.
`instance_tuples`	returns: instances :rtype: [(String, instance)]
`is_deferred_arguments`
`joined_paths`	Dot-joined path strings for each unique prior, ordered by id.
`label`
`mean_field`	Implements the same interface as the graphical code
`model_component_and_parameter_names`	Lists each parameter's name and path, and is used for labeling visualization with parameter labels.
`name`	The class name of this prior model (e.g. `"Model"` or `"Collection"`).
`order_no`	A string that can be used to order models by their parametrisation.
`parameter_labels`	Returns a list of labels containing latex labels of every parameter in a model.
`parameter_labels_with_superscripts`	Returns a list of the latex parameter label and superscript of every parameter in a model.
`parameter_labels_with_superscripts_latex`	Returns a list of the latex parameter label and superscript of every parameter in a model.
`parameter_names`	Returns a list of labels containing the name of every parameter in a model.
`parameterization`	Describes the path to each of the PriorModels, its class and its number of free parameters
`path_float_tuples`	(path, float) tuples for all fixed float values, excluding Prior objects.
`path_priors_tuples`	All (path, prior) tuples in this model, sorted by prior id.
`paths`	A list of paths to all the priors in the model, ordered by their ids
`paths_formatted`	A list of paths to all the priors in the model, ordered by their ids
`physical_values_from_prior_medians`	returns: physical_values -- A list of physical values constructed by taking the mean possible value from
`prior_class_dict`	Map each prior to the class it will produce when instantiated.
`prior_count`	How many unique priors does this model contain?
`prior_model_tuples`	(name, prior_model) tuples for direct child AbstractPriorModel attributes.
`prior_prior_model_dict`	returns: prior_prior_model_dict -- A dictionary mapping priors to associated prior models. Each prior will only
`prior_tuples`	returns: priors :rtype: [(String, Prior))]
`prior_tuples_ordered_by_id`	returns: priors -- An ordered list of unique priors associated with this mapper :rtype: [Prior]
`priors`	A list of all Prior objects in this model (may contain duplicates for shared priors).
`priors_ordered_by_id`	Unique priors sorted by their id, defining the canonical parameter ordering.
`random_vector_from_priors`	Generate a random vector of physical values by drawing uniform random values between 0 and 1 and using the model priors to map them from unit values to physical values.
`superscripts`	Returns a list of the model component superscripts for every parameter in a model.
`superscripts_overwrite_via_config`	Returns a list of the model component superscripts for every parameter in a model, which can be used to overwrite the default superscripts used in the function above.
`total_free_parameters`	Returns the prior count, but with a name that is more easy to interpret for users.
`tuple_prior_tuples`	returns: tuple_prior_tuples :rtype: [(String, TuplePrior)]
`unique_path_prior_tuples`	(path, prior) tuples deduplicated by prior identity, ordered by id.
`unique_prior_paths`	Paths to each unique prior (deduplicated for shared priors), ordered by id.
`unique_prior_tuples`	returns: prior_tuple_dict -- The set of all priors associated with this mapper :rtype: [(Prior, PriorTuple)]

property name#: The class name of this prior model (e.g. "Model" or "Collection").

property direct_argument_names: List[str]#: The names of priors, constants and other attributes that are direct attributes of this model.

instance_flatten(instance)[source]#: Flatten an instance of this model as a PyTree.

instance_unflatten(aux_data, children)[source]#

Unflatten a PyTree into an instance of this model.

Parameters:

aux_data
children

Return type:

An instance of this model.

tree_flatten()[source]#: Flatten this model as a PyTree.

classmethod tree_unflatten(aux_data, children)[source]#: Unflatten a PyTree into a model.

dict()[source]#: A dictionary representation of this object

property constructor_argument_names: List[str]#: The argument names of the constructor of the class of this model.

make_prior(attribute_name)[source]#

Returns a prior for an attribute of a class with a given name. The prior is created by searching the default prior config for the attribute.

Entries in configuration with a u become uniform priors; with a g become gaussian priors; with a c become instances.

If prior configuration for a given attribute is not specified in the configuration for a class then the configuration corresponding to the parents of that class is searched. If no configuration can be found then a prior exception is raised.

Parameters:: attribute_name (str) – The name of the attribute for which a prior is created
Returns:: prior – A prior
Return type:: p.Prior
Raises:: exc.PriorException – If no configuration can be found

gaussian_prior_model_for_arguments(arguments)[source]#

Returns a new instance of model mapper with a set of Gaussian priors based on tuples provided by a previous nonlinear search.

Parameters:: arguments ([(float, float)]) – Tuples providing the mean and sigma of gaussians
Returns:: new_model – A new model mapper populated with Gaussian priors
Return type:: ModelMapper