pyhs3.distributions.PolynomialDist

class pyhs3.distributions.PolynomialDist(**data)

Polynomial probability distribution.

Implements a polynomial probability density function as defined in ROOT’s RooPolynomial and the HS3 specification:

\[f(x; a_0, a_1, a_2, ...) = \frac{1}{\mathcal{M}} \sum_{i=0}^n a_i x^i = a_0 + a_1 x + a_2 x^2 + ...\]

Parameters:

• x (str) – Input variable name.

• coefficients (list[str]) – Array of coefficient parameter names.

Note

The degree of the polynomial is determined by the length of the coefficients array. ROOT uses a lowestOrder parameter to handle default coefficients, but for simplicity we require all coefficients to be explicitly specified.

ROOT Reference:

:rootref:`RooPolynomial <classRooPolynomial.html>`

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)	Builds a symbolic expression for the polynomial PDF.
log_expression(context)	Log-probability combining main likelihood with extended terms.
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.
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
x
coefficients
name