pyhs3.Workspace

class pyhs3.Workspace(**data)[source]

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

HS3 Reference

See HS3 file format specification for the complete workspace structure.

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, ...])

format_validation_error(validation_error, ...)

Format a ValidationError into a readable error summary.

from_orm(obj)

json(*[, include, exclude, by_alias, ...])

load(path, *[, verbose, suppress_traceback])

Load workspace from a JSON file.

model()

Constructs a Model rooted at target.

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, ensure_ascii, ...])

!!! 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(_Workspace__context, /)

Resolve foreign key references after workspace construction.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, extra, ...])

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

classmethod Workspace.construct(_fields_set=None, **values)
Parameters:
Return type:

Self

Workspace.copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

  • include (Set[int] | Set[str] | Mapping[int, Any] | Mapping[str, Any] | None) – Optional set or mapping specifying which fields to include in the copied model.

  • exclude (Set[int] | Set[str] | Mapping[int, Any] | Mapping[str, Any] | None) – Optional set or mapping specifying which fields to exclude in the copied model.

  • update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.

  • deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.

Return type:

Self

Returns:

A copy of the model with included, excluded and updated fields as specified.

Workspace.dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
Parameters:
Return type:

Dict[str, Any]

classmethod Workspace.format_validation_error(validation_error, path, verbose)[source]

Format a ValidationError into a readable error summary.

Parameters:

  • validation_error (ValidationError) – The ValidationError to format

  • path (str | PathLike[str]) – Path to the file that caused the error

  • verbose (bool) – If True, show all errors. If False, show first 20 and summarize rest.

Return type:

str

Returns:

Formatted error message string

classmethod Workspace.from_orm(obj)
Parameters:

obj (Any)

Return type:

Self

Workspace.json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
Parameters:
Return type:

str

classmethod Workspace.load(path, *, verbose=False, suppress_traceback=True)[source]

Load workspace from a JSON file.

Parameters:

  • path (str | PathLike[str]) – Path to the JSON file containing the HS3 specification

  • verbose (bool) – If True, show all errors. If False, show first 20 and summarize rest.

  • suppress_traceback (bool) – If True, suppress traceback on validation errors (default True).

Returns:

The loaded workspace instance

Return type:

Workspace

Workspace.model(target, *, domain=None, parameter_set=None, progress=True, mode='FAST_RUN')[source]
Workspace.model(target, *, parameter_set=None, progress=True, mode='FAST_RUN')
Workspace.model(target, *, domain=None, parameter_set=None, progress=True, mode='FAST_RUN')
Workspace.model(target, *, domain=None, parameter_set=None, progress=True, mode='FAST_RUN')

Constructs a Model rooted at target.

Dispatch is based on the type of target:

  • Analysis — all context (domain, parameter set, observables) is derived from the analysis; gains access to log_prob, data, and free_params. Only the likelihood’s distributions and their transitive dependencies are built.

  • Likelihood — observable bounds are derived from the likelihood’s data; domain falls back to default_domain then index 0, and parameter_set falls back to workspace defaults unless overridden. Only the likelihood’s distributions and their transitive dependencies are built.

  • str — searches analyses then likelihoods by name; delegates to the appropriate registered path. Falls back to legacy domain indexing if the name is not found in either.

  • int — legacy path: target indexes into workspace domains and the complete workspace graph is built.

Parameters:

  • target (int) – Dispatch key. Pass an Analysis or Likelihood for the modern paths, a name string to search analyses/likelihoods, or an int domain index for the legacy path.

  • domain (int | str | Domain | None) – Override domain (legacy and Likelihood paths only).

  • parameter_set (int | str | ParameterSet | None) – Override parameter set (legacy and Likelihood paths only).

  • progress (bool) – Whether to show a progress bar during graph construction.

  • mode (str) – PyTensor compilation mode (default "FAST_RUN").

Returns:

The constructed model.

Return type:

Model

classmethod Workspace.model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

  • _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

  • values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

Workspace.model_copy(*, update=None, deep=False)
!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

  • update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

  • deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

Workspace.model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

  • mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

  • include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to include in the output.

  • exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to exclude from the output.

  • context (Any | None) – Additional context to pass to the serializer.

  • by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

  • exclude_none (bool) – Whether to exclude fields that have a value of None.

  • exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

Workspace.model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

  • indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • ensure_ascii (bool) – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

  • include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to include in the JSON output.

  • exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to exclude from the JSON output.

  • context (Any | None) – Additional context to pass to the serializer.

  • by_alias (bool | None) – Whether to serialize using field aliases.

  • exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

  • exclude_none (bool) – Whether to exclude fields that have a value of None.

  • exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

  • round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

classmethod Workspace.model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation', *, union_format='any_of')

Generates a JSON schema for a model class.

Parameters:

  • by_alias (bool) – Whether to use attribute aliases or not.

  • ref_template (str) – The reference template.

  • union_format (Literal['any_of', 'primitive_type_array']) –

    The format to use when combining schemas from unions together. Can be one of:

    keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

  • schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod Workspace.model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

Workspace.model_post_init(_Workspace__context, /)[source]

Resolve foreign key references after workspace construction.

Parameters:

_Workspace__context (Any)

Return type:

None

classmethod Workspace.model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

  • force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors (bool) – Whether to raise errors, defaults to True.

  • _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

  • _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod Workspace.model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

  • obj (Any) – The object to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • from_attributes (bool | None) – Whether to extract data from object attributes.

  • context (Any | None) – Additional context to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod Workspace.model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)
!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data (str | bytes | bytearray) – The JSON data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context (Any | None) – Extra variables to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod Workspace.model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

  • obj (Any) – The object containing string data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context (Any | None) – Extra variables to pass to the validator.

  • by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

  • by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod Workspace.parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
Parameters:
Return type:

Self

classmethod Workspace.parse_obj(obj)
Parameters:

obj (Any)

Return type:

Self

classmethod Workspace.parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
Parameters:
Return type:

Self

classmethod Workspace.schema(by_alias=True, ref_template='#/$defs/{model}')
Parameters:

  • by_alias (bool)

  • ref_template (str)

Return type:

Dict[str, Any]

classmethod Workspace.schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
Parameters:

  • by_alias (bool)

  • ref_template (str)

  • dumps_kwargs (Any)

Return type:

str

classmethod Workspace.update_forward_refs(**localns)
Parameters:

localns (Any)

Return type:

None

classmethod Workspace.validate(value)
Parameters:

value (Any)

Return type:

Self

Workspace.model_computed_fields = {}
Workspace.model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Workspace.model_extra

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

Workspace.model_fields = {'analyses': FieldInfo(annotation=Union[Analyses, NoneType], required=False, default_factory=<lambda>), 'data': FieldInfo(annotation=Union[Data, NoneType], required=False, default_factory=<lambda>), 'distributions': FieldInfo(annotation=Union[Distributions, NoneType], required=False, default_factory=<lambda>), 'domains': FieldInfo(annotation=Union[Domains, NoneType], required=False, default_factory=<lambda>), 'functions': FieldInfo(annotation=Union[Functions, NoneType], required=False, default_factory=<lambda>), 'likelihoods': FieldInfo(annotation=Union[Likelihoods, NoneType], required=False, default_factory=<lambda>), 'metadata': FieldInfo(annotation=Metadata, required=True), 'misc': FieldInfo(annotation=Union[dict[str, Any], NoneType], required=False, default_factory=dict), 'parameter_points': FieldInfo(annotation=Union[ParameterPoints, NoneType], required=False, default_factory=<lambda>)}
Workspace.model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

Workspace.metadata: Metadata
Workspace.distributions: Distributions | None
Workspace.functions: Functions | None
Workspace.domains: Domains | None
Workspace.parameter_points: ParameterPoints | None
Workspace.data: Data | None
Workspace.likelihoods: Likelihoods | None
Workspace.analyses: Analyses | None
Workspace.misc: dict[str, Any] | None