Parameter

Parameter schema — bounds, transforms, priors, and initial values for a single parameter. Mirrors ionworkspipeline.Parameter.

Schemas for parameter.

class ionworks_schema.parameter.Parameter(name, initial_value=None, bounds=None, prior=None, normalize=None, check_bounds=None, check_initial_value=None, initial_guess_distribution=None)

Bases: BaseSchema

A model parameter that is free to move during a data fit.

Wrap any quantity you want the fit to adjust in a Parameter so the pipeline knows it’s a free variable: its starting value, its plausible range, and (optionally) what you already believe about its likely value. Anything not wrapped in a Parameter is treated as a fixed input.

Parameters

namestr

The name of the parameter. Should match the name used inside the model (e.g. "Particle diffusion time [s]").

initial_valuefloat | None, optional

The value the optimiser starts from. If you leave it unset, the midpoint of finite bounds is used (or 1 when there are no bounds).

boundstuple[float, float] | list[float] | None, optional

(lower, upper) tuple bracketing where you believe the true value lies. Defaults to no bounds. The upper bound must be strictly greater than the lower bound.

priorDistribution or Prior or None, optional

A probability distribution describing what you already believe about the parameter. Used by Bayesian and regularised fits.

normalizebool | None, optional

Rescale by the initial value before optimisation so the optimiser sees comparable magnitudes. Defaults to True.

check_boundsbool | None, optional

Validate the bounds at construction time. Defaults to True.

check_initial_valuebool | None, optional

Validate that the initial value falls inside the bounds at construction time. Defaults to True.

initial_guess_distributionDistribution or None, optional

When running multistart fits, this is the distribution the starting points are drawn from. Defaults to a uniform distribution over the bounds.

Examples

>>> # build the parameter with bounds, a prior, and a log transform
>>> raw = iws.Parameter(
...     "Negative particle diffusivity [m2.s-1]",
...     initial_value=1e-14,
...     bounds=(1e-16, 1e-12),
...     prior=iws.stats.LogNormal(mean=-32.2, std=2.0),
... )
>>> param = iws.transforms.Log10(parameter=raw)
>>> # slot it into a DataFit
>>> obj = iws.objectives.Pulse(data_input="path/to/gitt.csv")
>>> fit = iws.DataFit(objectives={"gitt": obj}, parameters={raw.name: param})

Extends: ionworks_schema.base.BaseSchema

See also: ionworkspipeline.Parameter (runtime implementation).

name: str

initial_value: int | float | None

bounds: tuple[int | float, int | float] | list[int | float] | None

prior: Any | None

normalize: bool | None

check_bounds: bool | None

check_initial_value: bool | None

initial_guess_distribution: Any | None

upper_bound_greater_than_lower()

Validate that upper bound is greater than lower bound.

model_config = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}

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

model_post_init(context: Any, /) → None

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.

Args:

self: The BaseModel instance. context: The context.