pyhs3.distributions.CrystalBallDist

class pyhs3.distributions.CrystalBallDist(**data)[source]

Single-sided Crystal Ball distribution implementation.

Implements the ROOT RooCrystalBall lineshape with a single power-law tail. This is the standard Crystal Ball distribution with shared parameters for both sides of the Gaussian core, but only one tail (usually on the left).

Mathematical Form:

\[\begin{split}f(m; m_0, \sigma, \alpha, n) = \begin{cases} A \cdot \left(B - \frac{m - m_0}{\sigma}\right)^{-n}, & \text{for } \frac{m - m_0}{\sigma} < -\alpha \\ \exp\left(-\frac{1}{2} \cdot \left[\frac{m - m_0}{\sigma}\right]^2\right), & \text{otherwise} \end{cases}\end{split}\]

where:

\[\begin{split}\begin{align} A &= \left(\frac{n}{\alpha}\right)^{n} \cdot \exp\left(-\frac{\alpha^2}{2}\right) \\ B &= \frac{n}{\alpha} - \alpha \end{align}\end{split}\]
Parameters:

  • m – Observable variable

  • m0 – Peak position (mean)

  • sigma – Width parameter (must be > 0)

  • alpha – Transition point (must be > 0)

  • n – Power law exponent (must be > 0)

Note

All parameters except m and m0 must be positive. This is the standard single-sided Crystal Ball used widely in high-energy physics.

HS3 Reference

crystalball_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.

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 single-sided Crystal Ball distribution.

log_expression(context)

Log-probability combining main likelihood with extended terms.

log_likelihood(context)

Builds a symbolic expression for the single-sided Crystal Ball log-PDF.

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])

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

alpha

m

m0

n

sigma

name

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

Self

CrystalBallDist.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.

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

Dict[str, Any]

CrystalBallDist.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

CrystalBallDist.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 CrystalBallDist.from_orm(obj)
Parameters:

obj (Any)

Return type:

Self

CrystalBallDist.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']
CrystalBallDist.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

CrystalBallDist.likelihood(context)[source]

Evaluate the single-sided Crystal Ball distribution.

Implements the ROOT RooCrystalBall formula with a single tail. All shape parameters (alpha, n, sigma) are assumed to be positive.

Parameters:

context (Context)

Return type:

TypeAliasType

CrystalBallDist.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

CrystalBallDist.log_likelihood(context)[source]

Builds a symbolic expression for the single-sided Crystal Ball log-PDF.

Analytic log form of likelihood(), piecewise over the same core/tail split:

\[\begin{split}\log f(m) = \begin{cases} \log A - n \log(B - t), & t < -\alpha \\ -\frac{1}{2} t^2, & \text{otherwise} \end{cases}\end{split}\]

via _crystalball_log_tail() for the tail branch. Evaluating this directly (rather than pt.log(self.likelihood(...))) avoids computing the power-law tail \(A(B-t)^{-n}\) and re-logging it, which underflows to 0.0 (and then to -inf) once the tail decays far enough, and avoids computing \(\exp(-t^2/2)\) in the core and re-logging that, which underflows for large \(|t|\).

Parameters:

context (dict) – Mapping of names to pytensor variables.

Returns:

Symbolic representation of the single-sided Crystal Ball log-PDF.

Return type:

pytensor.tensor.variable.TensorVariable

CrystalBallDist.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 CrystalBallDist.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.

CrystalBallDist.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.

CrystalBallDist.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.

CrystalBallDist.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 CrystalBallDist.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 CrystalBallDist.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.

CrystalBallDist.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 CrystalBallDist.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 CrystalBallDist.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 CrystalBallDist.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 CrystalBallDist.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.

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

Self

classmethod CrystalBallDist.parse_obj(obj)
Parameters:

obj (Any)

Return type:

Self

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

Self

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

  • by_alias (bool)

  • ref_template (str)

Return type:

Dict[str, Any]

classmethod CrystalBallDist.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 CrystalBallDist.update_forward_refs(**localns)
Parameters:

localns (Any)

Return type:

None

classmethod CrystalBallDist.validate(value)
Parameters:

value (Any)

Return type:

Self

CrystalBallDist.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.

CrystalBallDist.model_computed_fields = {}
CrystalBallDist.model_config: ClassVar[ConfigDict] = {'serialize_by_alias': True}

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

CrystalBallDist.model_extra

Get extra fields set during validation.

Returns:

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

CrystalBallDist.model_fields = {'alpha': FieldInfo(annotation=str, required=True), 'm': FieldInfo(annotation=str, required=True), 'm0': FieldInfo(annotation=str, required=True), 'n': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True, json_schema_extra={'preprocess': False}), 'sigma': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Literal['crystalball_dist'], required=False, default='crystalball_dist')}
CrystalBallDist.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.

CrystalBallDist.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.

CrystalBallDist.type: Literal['crystalball_dist']
CrystalBallDist.alpha: str
CrystalBallDist.m: str
CrystalBallDist.m0: str
CrystalBallDist.n: str
CrystalBallDist.sigma: str
CrystalBallDist.name: str