pyhs3.Workspace

class pyhs3.Workspace(**data)

Workspace for managing HS3 model specifications.

A workspace contains parameter points, distributions, domains, and functions that define a probabilistic model. It provides methods to construct Model objects with specific parameter values and domain constraints.

metadata

Required metadata containing HS3 version and optional attribution

distributions

List of distribution configurations

functions

List of function configurations

domains

List of domain configurations

parameter_points

List of parameter point configurations

data

Data specifications for observations

likelihoods

Likelihood specifications mapping distributions to data

analyses

Analysis configurations for automated analyses

misc

Arbitrary user-created information

parameter_collection

Named parameter sets.

Type:

ParameterPoints

distribution_set

Available distributions.

Type:

Distributions

domain_collection

Domain constraints for parameters.

Type:

Domains

function_set

Available functions for parameter computation.

Type:

Functions

Parameters:

data (Any)

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
load(path, *[, verbose, suppress_traceback])	Load workspace from a JSON file.
model(*[, domain, parameter_set, progress, mode])	Constructs a Model object using the provided domain and parameter set.
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, include, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
metadata
distributions
functions
domains
parameter_points
data
likelihoods
analyses
misc