autofit.Collection

class autofit.Collection(*arguments, **kwargs)

Bases: AbstractPriorModel

The object multiple Python classes are input into to create model-components, which has free parameters that are fitted by a non-linear search.

Multiple Python classes can be input into a Collection in order to compose high dimensional models made of multiple model-components.

The Collection object is highly 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 document more clearly]

A prior model used to represent a list of prior models for convenience.

Arguments are flexibly converted into a collection.

Parameters:

arguments – Classes, prior models, instances or priors

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.Collection(gaussian_0=Gaussian, gaussian_1=Gaussian)

Methods

add_assertion	Assert that some relationship holds between physical values associated with priors at the point an instance is created.
add_dict_items	Add all entries from a dictionary, converting values to prior models.
all_paths_for_prior	Find all paths that points at the given tuple.
append	Append an item to the collection with an auto-incremented numeric key.
as_model	Convert all prior models in this collection to Model instances.
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	Create a new collection, updating its priors according to the argument dictionary.
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_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.
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	The (key, model_component) pairs in this collection.
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.
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	Construct a name for the prior.
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.
remove	Remove an item from the collection by value equality.
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 collection into a JAX-compatible PyTree representation.
tree_unflatten	Reconstruct a Collection from a flattened PyTree.
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.
with_prefix	Filter members of the collection, only returning those that start with a given prefix as a new collection.
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.
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)]
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)]
values	The model components in this collection as a list.

name_for_prior(prior: Prior) → str

Construct a name for the prior. This is the path taken to get to the prior.

Parameters:

prior

Return type:

A string of object names joined by underscores

tree_flatten()

Flatten this collection into a JAX-compatible PyTree representation.

Returns:

A (children, aux_data) pair where children are the values and aux_data are the corresponding keys.

Return type:

tuple

classmethod tree_unflatten(aux_data, children)

Reconstruct a Collection from a flattened PyTree.

Parameters:

• aux_data – The keys of the collection items.

• children – The values of the collection items.

property values

The model components in this collection as a list.

items()

The (key, model_component) pairs in this collection.

with_prefix(prefix: str)

Filter members of the collection, only returning those that start with a given prefix as a new collection.

as_model()

Convert all prior models in this collection to Model instances.

Returns a new Collection where each AbstractPriorModel child has been converted via its own as_model() method.

add_dict_items(item_dict)

Add all entries from a dictionary, converting values to prior models.

Parameters:

item_dict – A dictionary mapping string keys to classes, instances, or prior models.

append(item)

Append an item to the collection with an auto-incremented numeric key.

The item is converted to an AbstractPriorModel if it is not already one.

remove(item)

Remove an item from the collection by value equality.

Parameters:

item – The item to remove. All entries whose value equals this item are deleted.

gaussian_prior_model_for_arguments(arguments)

Create a new collection, updating its priors according to the argument dictionary.

Parameters:

arguments – A dictionary of arguments

Return type:

A new collection

property prior_class_dict

Map each prior to the class it will produce when instantiated.

For child prior models, delegates to their own prior_class_dict. Direct priors on the collection itself map to ModelInstance.