pyhs3.distributions.ProductDist¶

class pyhs3.distributions.ProductDist(**data)[source]¶

Product distribution implementation.

Implements a product of PDFs as defined in ROOT’s RooProdPdf.

The probability density function is defined as:

\[f(x, \ldots) = \prod_{i=1}^{N} \text{PDF}_i(x, \ldots)\]

where each PDF_i is a component distribution that may share observables.

Parameters:: factors – List of component distribution names to multiply together

Note

In the context of pytensor variables/tensors, this is implemented as an elementwise product of all factor distributions.

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, ...])
`expression`(context)	Evaluate and return a named PyTensor expression.
`extended_likelihood`(_context[, _data])	Extended likelihood contribution in normal space.
`from_orm`(obj)
`get_parameter_list`(context, param_key)	Reconstruct a parameter list from flattened indexed keys.
`json`(*[, include, exclude, by_alias, ...])
`likelihood`(context)	Evaluate the product distribution.
`log_expression`(context)	Log-probability combining main likelihood with extended terms.
`log_likelihood`(context)	Log-space counterpart of `likelihood()`.
`log_prob_terms`(expressions, log_expressions, ...)	Split factors into per-event shape terms and per-channel constraints.
`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`(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
`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.
`normalization_expression`(_context, ...)	Return the antiderivative expression, or None for numerical fallback.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`process_parameter`(param_key)	Process a single parameter that can be either a string reference or numeric value.
`process_parameter_list`(param_key)	Process a list parameter containing mixed string references and numeric values.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`constants`	Dictionary of PyTensor constants generated from numeric field values.
`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.
`parameters`	Set of parameter names this component depends on.
`type`
`factors`
`name`

classmethod ProductDist.construct(_fields_set=None, **values)¶

Parameters:

_fields_set (set[str] | None)
values (Any)

Return type:

Self

ProductDist.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.

ProductDist.dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)¶

Parameters:

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)
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)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)

Return type:

Dict[str, Any]

ProductDist.expression(context)¶

Evaluate and return a named PyTensor expression.

This is a template method that calls _expression() to get the result, then names the result before returning.

_expression() may return a tensor it does not own — for example a single-summand SumFunction or a single-factor ProductFunction returns context[param] directly, which is the same tensor object stored in model.parameters. Renaming such a shared tensor in place would silently corrupt the parameter it borrows (e.g. model.parameters['mu'] would be renamed to the function’s name). To avoid this, a result that already carries a different name is wrapped in a fresh identity copy before being renamed; freshly built (unnamed) expressions are named in place as before so that compilation and graph_summary keep working.

Parameters:: context (Context) – Mapping of names to PyTensor variables
Return type:: TypeAliasType
Returns:: Named PyTensor expression representing the component

ProductDist.extended_likelihood(_context, _data=None)¶

Extended likelihood contribution in normal space.

Returns additional likelihood terms for extended ML fitting. Override only when the extended terms belong in the distribution’s per-event density, like constraint terms (HistFactory). Terms that enter the likelihood once per channel (e.g. the Poisson yield term of an extended MixtureDist, which involves the observed event count) must not use this hook: _expression() multiplies the result into the density, so Model.log_prob would count it once per event when summing over data. Such terms are assembled once per channel by Model.log_prob, which owns the channel-dataset pairing.

Default: no contribution (returns 1.0 in normal space).

Parameters:

context – Mapping of names to pytensor variables
data – Optional data tensor for data-dependent terms
_context (Context)
_data (TypeAliasType | None)

Returns:

Likelihood contribution (default: 1.0 = no contribution)

Return type:

TensorVar

classmethod ProductDist.from_orm(obj)¶

Parameters:: obj (Any)
Return type:: Self

ProductDist.get_parameter_list(context, param_key)¶

Reconstruct a parameter list from flattened indexed keys.

Used to recover the original list structure from the indexed parameter mapping created by process_parameter_list().

Parameters:

context (Context) – The context containing parameter values mapped by name.
param_key (str) – The base parameter key (e.g., “factors”).

Return type:

list[TypeAliasType]

Returns:

List of parameter values in original order.

Example

>>> from typing import Literal
>>> class TestEvaluable(Evaluable):
...     type: Literal["test"] = "test"
...     factors: list[str | float]
...     def _expression(self, _: Context) -> TensorVar:
...         return None
>>>
>>> eval1 = TestEvaluable(name="test", factors=["a", 1.0, "b"])
>>> context = {
...     "a": "tensor_a",
...     "constant_test_factors[1]": "tensor_1",
...     "b": "tensor_b"
... }
>>> eval1.get_parameter_list(context, "factors")
['tensor_a', 'tensor_1', 'tensor_b']

ProductDist.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:

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)
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)
by_alias (bool)
exclude_unset (bool)
exclude_defaults (bool)
exclude_none (bool)
encoder (Callable[[Any], Any] | None)
models_as_dict (bool)
dumps_kwargs (Any)

Return type:

str

ProductDist.likelihood(context)[source]¶

Evaluate the product distribution.

Parameters:: context (Context) – Mapping of names to pytensor variables
Return type:: TypeAliasType
Returns:: Symbolic representation of the product PDF

ProductDist.log_expression(context)¶

Log-probability combining main likelihood with extended terms.

Returns log_likelihood() plus log(extended_likelihood()), minus the log of the normalization integral where normalization applies. This is mathematically equivalent to log(likelihood * extended_likelihood) but starts from log_likelihood() so that a distribution’s analytic log form (e.g. Gaussian, Poisson, LogNormal) is used directly instead of round- tripping through a probability value that can underflow to 0.0.

Normalization itself stays probability-space: the normalization integral (whether analytic or Gauss-Legendre quadrature) is a sum of positive terms and is far less underflow-prone than the raw density, so log(integral) is subtracted rather than computed as its own analytic log form.

All distributions are automatically normalized over observables present in the context, unless explicitly opted out via _normalizable = False.

PyTensor handles optimization and simplification automatically.

Parameters:: context (Context) – Mapping of names to pytensor variables
Returns:: Log-probability density
Return type:: TensorVar

ProductDist.log_likelihood(context)¶

Log-space counterpart of likelihood().

Default implementation takes the log of the probability-space likelihood. Override with an analytic log-space form wherever one exists (e.g. Gaussian, Poisson, LogNormal): the default round-trips through a probability value that can underflow to 0.0 for parameter points far from the mode, at which point pt.log(0.0) is -inf even though the true log-density is finite.

Parameters:: context (Context) – Mapping of names to pytensor variables
Returns:: Log of the main probability density
Return type:: TensorVar

ProductDist.log_prob_terms(expressions, log_expressions, distributions)[source]¶

Split factors into per-event shape terms and per-channel constraints.

Factors that depend on an observable (detected by a pt.vector free input — observable arrays are built as pt.vector, NP scalars as pt.scalar) delegate to their own log_prob_terms, so e.g. an extended mixture inside a product contributes its yield term correctly.

Factors that depend only on scalar nuisance parameters are constraint PDFs and are returned as named constraints for the model to add once globally. This prevents the N-fold overcounting that occurs when naively summing log(shape x Π_j constr_j) over N events, which multiplies each constr_j term by N rather than counting it once.

The shape-vs-constraint classification is structural inference (matching RooProdPdf), not HS3 spec semantics — nothing in an HS3 file marks a factor as a constraint. If the spec adopts explicit constraint encoding, this inference can be removed; see https://github.com/hep-statistics-serialization-standard/hep-statistics-serialization-standard/issues/90 and https://github.com/scipp-atlas/pyhs3/issues/240.

Parameters:

expressions (Mapping[str, TypeAliasType])
log_expressions (Mapping[str, TypeAliasType])
distributions (Distributions)

Return type:

LogProbTerms

classmethod ProductDist.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.

ProductDist.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.

ProductDist.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.

ProductDist.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 ProductDist.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:
- ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
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 ProductDist.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.

ProductDist.model_post_init(context, /)¶

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

classmethod ProductDist.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 ProductDist.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 ProductDist.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 ProductDist.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.

ProductDist.normalization_expression(_context, _observable_name)¶

Return the antiderivative expression, or None for numerical fallback.

Override in subclasses to provide analytical normalization. The returned expression should be the antiderivative F(x) such that the integral ∫f(x)dx from a to b equals F(b) - F(a).

Parameters:

context – Mapping of names to pytensor variables
observable_name – Name of the observable to integrate over
_context (Context)
_observable_name (str)

Return type:

TypeAliasType | None

Returns:

Symbolic antiderivative expression, or None for numerical fallback.

classmethod ProductDist.parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)¶

Parameters:

path (str | Path)
content_type (str | None)
encoding (str)
proto (Protocol | None)
allow_pickle (bool)

Return type:

Self

classmethod ProductDist.parse_obj(obj)¶

Parameters:: obj (Any)
Return type:: Self

classmethod ProductDist.parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)¶

Parameters:

b (str | bytes)
content_type (str | None)
encoding (str)
proto (Protocol | None)
allow_pickle (bool)

Return type:

Self

ProductDist.process_parameter(param_key)¶

Process a single parameter that can be either a string reference or numeric value.

For numeric values, generates a unique constant name. For string values, returns the value as-is.

Parameters:

param_key (str) – The parameter field name to process (e.g., “mean”, “sigma”).

Returns:

processed_name: Either the original string value or a generated constant name
numeric_value: The numeric value if input was numeric, None otherwise

Return type:

Tuple containing

Example

>>> from typing import Literal
>>> class TestEvaluable(Evaluable):
...     type: Literal["test"] = "test"
...     some_param: str | float
...     def _expression(self, _: Context) -> TensorVar:
...         return None
>>>
>>> # String parameter
>>> eval1 = TestEvaluable(name="test1", some_param="alpha")
>>> eval1.process_parameter("some_param")
('alpha', None)

>>> # Numeric parameter
>>> eval2 = TestEvaluable(name="test2", some_param=1.5)
>>> eval2.process_parameter("some_param")
('constant_test2_some_param', 1.5)

ProductDist.process_parameter_list(param_key)¶

Process a list parameter containing mixed string references and numeric values.

For numeric values, generates indexed unique names and stores the values. For string values, returns the values as-is. Also updates internal parameter mapping with indexed keys.

Parameters:

param_key (str) – The parameter field name to process (e.g., “factors”, “coefficients”).

Returns:

processed_names: List of parameter names (original strings or generated constant names)
numeric_values: List of numeric values (None for string entries)

Return type:

Tuple containing

Example

>>> from typing import Literal
>>> class TestEvaluable(Evaluable):
...     type: Literal["test"] = "test"
...     factors: list[str | float]
...     def _expression(self, _: Context) -> TensorVar:
...         return None
>>>
>>> eval1 = TestEvaluable(name="test", factors=["param1", 2.0, "param2"])
>>> names, values = eval1.process_parameter_list("factors")
>>> names
['param1', 'constant_test_factors[1]', 'param2']
>>> values
[None, 2.0, None]

classmethod ProductDist.schema(by_alias=True, ref_template='#/$defs/{model}')¶

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

classmethod ProductDist.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 ProductDist.update_forward_refs(**localns)¶

Parameters:: localns (Any)
Return type:: None

classmethod ProductDist.validate(value)¶

Parameters:: value (Any)
Return type:: Self

ProductDist.constants¶

Dictionary of PyTensor constants generated from numeric field values.

Returns:: Mapping from generated constant names to PyTensor constant tensors. Empty if all fields are string references.

ProductDist.model_computed_fields = {}¶

ProductDist.model_config: ClassVar[ConfigDict] = {'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

ProductDist.model_extra¶

Get extra fields set during validation.

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

ProductDist.model_fields = {'factors': FieldInfo(annotation=list[str], required=True), 'name': FieldInfo(annotation=str, required=True, json_schema_extra={'preprocess': False}), 'type': FieldInfo(annotation=Literal['product_dist'], required=False, default='product_dist')}¶

ProductDist.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.

ProductDist.parameters¶

Set of parameter names this component depends on.

Returns:: Set of parameter names, including both string references and generated constant names for numeric values.

ProductDist.type: Literal['product_dist']¶

ProductDist.factors: list[str]¶

ProductDist.name: str¶