pyhs3.distributions.HistFactoryDistChannel¶

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

HistFactory probability distribution for a single channel/region.

Implements binned statistical models consisting of histograms (step functions) with various modifiers as defined in the HS3 specification. Each HistFactoryDistChannel represents one independent measurement channel/region with its own observed data. Multiple channels can be combined in a workspace to form a complete HistFactory model.

The total likelihood consists of: 1. Main likelihood: Poisson likelihood for observed bin counts vs expected rates 2. Constraint likelihoods: Constraint terms for nuisance parameters

The prediction for a binned region is given as:

\[\lambda(x) = \sum_{s \in \text{samples}} \left[ \left( d_s(x) + \sum_{\delta \in M_\delta} \delta(x,\theta_\delta) \right) \prod_{\kappa \in M_\kappa} \kappa(x,\theta_\kappa) \right]\]

where:

$d_s(x)$ is the nominal prediction for sample $s$
$M_\delta$ are additive modifiers (histosys)
$M_\kappa$ are multiplicative modifiers (normfactor, normsys, shapefactor, etc.)

Observed Data Convention:

Observed data must be provided in the context as {name}_observed where name is the HistFactory distribution name. This is required for likelihood evaluation.

Constraint Types:

Gaussian constraints (default): histosys, normsys, staterror
Poisson constraints (default): shapesys
All constraint types can be overridden via the constraint field

Parameters:

axes (list) – Array of axis definitions with binning information
samples (list) – Array of sample definitions with data and modifiers

Supported Modifiers:

normfactor: Multiplicative scaling by parameter value
normsys: Multiplicative systematic with hi/lo interpolation
histosys: Additive correlated shape systematic
shapefactor: Uncorrelated multiplicative bin-by-bin scaling
shapesys: Uncorrelated shape systematic with Poisson constraints
staterror: Statistical uncertainty via Barlow-Beeston method

Modifier Naming in Dependency Graph:

Modifiers have simple names (e.g., “lumi”) in the HS3 specification, but are given unique identifiers in the dependency graph by prepending the full context: {dist_name}/{sample_name}/{modifier_type}/{modifier_name}

This design distinguishes individual modifier instances while allowing parameters to indicate correlation - modifiers sharing the same parameter name are correlated.

Example: Two modifiers both named “lumi” in different samples will have unique graph nodes like “SR/signal/normsys/lumi” and “CR/background/normsys/lumi”, but if they share the same parameter name, they are correlated.

HS3 Reference

histfactory_dist

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.
`constraint_specs`()	Yield `(dedup_key, modifier, sample_data)` for each constraint modifier.
`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])	Build constraint product for this channel.
`from_orm`(obj)
`get_internal_nodes`()	Return all internal nodes that need to be in the dependency graph.
`get_parameter_list`(context, param_key)	Reconstruct a parameter list from flattened indexed keys.
`json`(*[, include, exclude, by_alias, ...])
`likelihood`(context)	Build the HistFactory main Poisson likelihood.
`log_expression`(context)	Log-probability for the channel: summed Poisson log-pmf + log-constraints.
`log_extended_likelihood`(context)	Log-space constraint sum for this channel.
`log_likelihood`(context)	Log-space main Poisson likelihood: sum of per-bin Poisson log-pmfs.
`log_prob_terms`(_expressions, ...)	Structured contributions of this distribution to the joint log-likelihood.
`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])
`to_hist`()	Convert HistFactory channel to hist.Hist with categorical process axis.
`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`	Return all parameters used by this HistFactory distribution.
`type`
`axes`
`samples`
`barlow_beeston_method`
`name`

HistFactoryDistChannel.constraint_specs()[source]¶

Yield (dedup_key, modifier, sample_data) for each constraint modifier.

dedup_key is the modifier’s parameter name for single-parameter modifiers (normsys, histosys); callers may use it to dedup constraints when multiple modifier instances reference the same nuisance parameter — within a channel or across channels in a joint fit. For multi-parameter modifiers (shapesys, staterror) dedup_key is None — these constraints are channel-local by workspace validation and are always emitted as-is.

In BB-lite mode, StatErrorModifier specs are skipped entirely: their data is None by design (per-bin errors come from sample data instead), so modifier.make_constraint() would raise. The corresponding constraint is channel-level, not modifier-level — callers get it from _make_barlow_beeston_lite_constraint() instead.

Return type:: Iterator[tuple[str | None, HasConstraint, SampleData]]

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

Parameters:

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

Return type:

Self

HistFactoryDistChannel.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.

HistFactoryDistChannel.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]

HistFactoryDistChannel.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

HistFactoryDistChannel.extended_likelihood(context, _data=None)[source]¶

Build constraint product for this channel.

Constraints are deduped by parameter — multiple ParameterModifier instances sharing one nuisance parameter (e.g., two normsys on different samples both pointing at alpha_lumi) emit a single constraint factor, not one per modifier. ParametersModifier constraints (shapesys, staterror) carry per-bin nominal yields and are always emitted per-modifier.

Parameters:

context (Context)
_data (TypeAliasType | None)

Return type:

TypeAliasType

classmethod HistFactoryDistChannel.from_orm(obj)¶

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

HistFactoryDistChannel.get_internal_nodes()[source]¶

Return all internal nodes that need to be in the dependency graph.

Modifiers can have the same name across different samples/types (e.g., “Lumi” appearing in multiple places), but the dependency graph requires unique node identifiers. We create wrapper objects that provide unique names while delegating to the original modifier’s functionality.

Return type:: list[Any]

HistFactoryDistChannel.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']

HistFactoryDistChannel.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

HistFactoryDistChannel.likelihood(context)[source]¶

Build the HistFactory main Poisson likelihood.

Returns the Poisson probability for observed bin counts vs expected rates. Does NOT include constraint terms - those are added via extended_likelihood().

Parameters:: context (Context) – Mapping of parameter names to PyTensor variables
Return type:: TypeAliasType
Returns:: PyTensor expression for the main Poisson model probability

HistFactoryDistChannel.log_expression(context)[source]¶

Log-probability for the channel: summed Poisson log-pmf + log-constraints.

Overrides Distribution.log_expression() to assemble the channel log-probability directly in log space (sum of per-bin Poisson log-pmfs plus summed log-constraint terms), rather than taking log of the probability-space likelihood()/extended_likelihood() product. This keeps the result finite where the probability-space product would underflow to 0.0.

Parameters:: context (Context) – Mapping of parameter names to PyTensor variables
Return type:: TypeAliasType
Returns:: PyTensor expression for the channel log-probability.

HistFactoryDistChannel.log_extended_likelihood(context)[source]¶

Log-space constraint sum for this channel.

Log-space counterpart of extended_likelihood(): returns the sum of each modifier’s log_constraint(...) term (deduped by parameter exactly as in extended_likelihood()) instead of the product of make_constraint(...) terms. log_constraint evaluates the same constraint distribution(s) via their analytic log-space form, so this never takes pt.log of a probability-space value that can underflow to 0.0.

Parameters:: context (Context) – Mapping of parameter names to PyTensor variables
Return type:: TypeAliasType
Returns:: PyTensor expression for the summed log-constraint contribution.

HistFactoryDistChannel.log_likelihood(context)[source]¶

Log-space main Poisson likelihood: sum of per-bin Poisson log-pmfs.

This is the log-space counterpart of likelihood(). Returning the sum of per-bin log-probabilities directly avoids the log(prod(exp(log_probs))) round-trip, whose intermediate product underflows float64 to 0.0 for channels with many bins or large expected counts (turning log_prob into -inf).

Parameters:: context (Context) – Mapping of parameter names to PyTensor variables
Return type:: TypeAliasType
Returns:: PyTensor expression for the summed Poisson log-probability.

HistFactoryDistChannel.log_prob_terms(_expressions, log_expressions, _distributions)¶

Structured contributions of this distribution to the joint log-likelihood.

Called by pyhs3.Model.log_prob for each channel after the model graph is built. Override when the distribution’s terms do not all enter the likelihood as a per-event log-density — e.g. once-per- channel yield terms (extended MixtureDist) or globally-deduplicated constraint factors (ProductDist).

Default: a single per-event log(PDF) term sourced from the pre-built log-space expression so it stays finite where the probability-space PDF underflows.

Parameters:

_expressions (Mapping[str, TypeAliasType]) – Compiled symbolic expressions for all distributions, keyed by name (model.distributions). Unused in the base implementation; subclasses may reference it.
log_expressions (Mapping[str, TypeAliasType]) – Compiled log-space symbolic expressions for all distributions, keyed by name (model.log_distributions).
_distributions (Distributions) – Distribution objects keyed by name, so composite distributions can delegate to their components’ hooks.

Returns:

per-event, per-channel, and constraint contributions.

Return type:

LogProbTerms

classmethod HistFactoryDistChannel.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.

HistFactoryDistChannel.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.

HistFactoryDistChannel.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.

HistFactoryDistChannel.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 HistFactoryDistChannel.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 HistFactoryDistChannel.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.

HistFactoryDistChannel.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 HistFactoryDistChannel.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 HistFactoryDistChannel.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 HistFactoryDistChannel.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 HistFactoryDistChannel.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.

HistFactoryDistChannel.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 HistFactoryDistChannel.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 HistFactoryDistChannel.parse_obj(obj)¶

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

classmethod HistFactoryDistChannel.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

HistFactoryDistChannel.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)

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

Parameters:

by_alias (bool)
ref_template (str)

Return type:

Dict[str, Any]

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

Parameters:

by_alias (bool)
ref_template (str)
dumps_kwargs (Any)

Return type:

str

HistFactoryDistChannel.to_hist()[source]¶

Convert HistFactory channel to hist.Hist with categorical process axis.

Creates a single histogram combining all samples using a categorical axis. The first axis is a categorical axis with sample names (labeled “process”), followed by the original binning axes.

Returns:

Histogram with shape (n_samples, *binning_shape) where:

Axis 0: Categorical axis “process” with sample names
Remaining axes: Original binning axes from self.axes
Values: Sample contents (with errors as variances)

Return type:

hist.Hist

Examples

>>> channel = HistFactoryDistChannel(
...     name="SR",
...     axes=[{"name": "mass", "min": 100, "max": 150, "nbins": 5}],
...     samples=[
...         {"name": "signal", "data": {"contents": [105, 106, 107, 108, 109], "errors": [0.5, 0.6, 0.7, 0.8, 0.9]}},
...         {"name": "background", "data": {"contents": [110, 111, 112, 113, 114], "errors": [0.05, 0.06, 0.07, 0.08, 0.09]}}
...     ]
... )
>>> h = channel.to_hist()
>>> h
Hist(
  StrCategory(['signal', 'background'], name='process'),
  Regular(5, 100, 150, name='mass'),
  storage=Weight()) # Sum: WeightedSum(value=1095, variance=2.5755)
>>> h.axes[0]
StrCategory(['signal', 'background'], name='process')
>>> h["signal", :]  # Get all mass bins for signal sample
Hist(Regular(5, 100, 150, name='mass'), storage=Weight()) # Sum: WeightedSum(value=535, variance=2.55)

classmethod HistFactoryDistChannel.update_forward_refs(**localns)¶

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

classmethod HistFactoryDistChannel.validate(value)¶

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

HistFactoryDistChannel.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.

HistFactoryDistChannel.model_computed_fields = {}¶

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

HistFactoryDistChannel.model_extra¶

Get extra fields set during validation.

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

HistFactoryDistChannel.model_fields = {'axes': FieldInfo(annotation=BinnedAxes, required=True, json_schema_extra={'preprocess': False}), 'barlow_beeston_method': FieldInfo(annotation=Literal['full', 'lite'], required=False, default='lite', json_schema_extra={'preprocess': False}), 'name': FieldInfo(annotation=str, required=True, json_schema_extra={'preprocess': False}), 'samples': FieldInfo(annotation=Samples, required=True, json_schema_extra={'preprocess': False}), 'type': FieldInfo(annotation=Literal['histfactory_dist'], required=False, default='histfactory_dist')}¶

HistFactoryDistChannel.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.

HistFactoryDistChannel.parameters¶: Return all parameters used by this HistFactory distribution.

HistFactoryDistChannel.type: Literal['histfactory_dist']¶

HistFactoryDistChannel.axes: BinnedAxes¶

HistFactoryDistChannel.samples: Samples¶

HistFactoryDistChannel.barlow_beeston_method: Literal['full', 'lite']¶

HistFactoryDistChannel.name: str¶