Result¶

Every pipeline element reports through an ElementResult; the fitting results below extend it.

class ionworkspipeline.results.ElementResult(parameter_values: dict | None = None)¶

Base class for typed pipeline element results.

Subclasses set a unique type ClassVar and override to_config() to expose their payload fields.

Extends: ParameterValues

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

ionworkspipeline.Result¶: alias of ParameterEstimatorResult

class ionworkspipeline.ParameterEstimatorResult(parameter_values: dict | None = None, *, cost: float | None = None, samples: Any = None, costs: Any = None, callbacks: dict | None = None, callback_results: dict | None = None, children: list[ParameterEstimatorResult] | None = None, initial_guess: dict | None = None, job_id: int | None = None)¶

Abstract marker for DataFit-family results.

Extends: ElementResult

best_results(num_results: int | integer | None = None) → list[ParameterEstimatorResult]¶: Return best num_results children by ascending cost (None = all).

direct_samples: ClassVar[bool] = False¶: Class-level flag indicating that the parameter estimator populates samples and costs directly from a multi-point evaluation (PosteriorResult for MCMC, EnsembleResult for grid/point/batch eval). Optimizer-style results leave this False and DataFit fills samples from the cost-logger history instead.

get_fit_results()¶: Per-objective fit-result dicts from internal callbacks.

plot_fit_results()¶: Plot per-objective fit results.

replace(**overrides) → ParameterEstimatorResult¶: Return a new instance with overrides applied to init fields.

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

DataFit return for deterministic optimizers (CMAES, Scipy, …).

Extends: ParameterEstimatorResult

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

class ionworkspipeline.EnsembleResult(parameter_values: dict | None = None, *, x: Any = None, method: str | None = None, **kw)¶

DataFit return for ensemble-of-points evaluators (grid search, point estimate, batch point estimate).

Holds the parameter vectors evaluated (samples), their objective values (costs), and the best point (x). Mirrors OptimizationResult shape without optimizer-specific fields (success/message/evaluations/iterations) since these methods perform a fixed batch of evaluations rather than an iterative search.

Extends: ParameterEstimatorResult

direct_samples: ClassVar[bool] = True¶: Class-level flag indicating that the parameter estimator populates samples and costs directly from a multi-point evaluation (PosteriorResult for MCMC, EnsembleResult for grid/point/batch eval). Optimizer-style results leave this False and DataFit fills samples from the cost-logger history instead.

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

property x¶

Best (lowest-cost) sample row.

Falls back to samples[nanargmin(costs)] when not explicitly set.

class ionworkspipeline.PosteriorResult(parameter_values: dict | None = None, *, chains: Any = None, chains_storage_ref: str | None = None, log_pdfs: Any = None, parameter_names: list[str] | None = None, burnin: int = 0, method: str | None = None, acceptance_rate: float | None = None, r_hat: dict[str, float] | None = None, ess: dict[str, float] | None = None, posterior_summary: dict[str, dict[str, float]] | None = None, **kw)¶

DataFit return for MCMC samplers (e.g. Pints).

Extends: ParameterEstimatorResult

credible_interval(name: str, level: float = 0.95) → tuple[float, float]¶: Equal-tailed credible interval at level.

direct_samples: ClassVar[bool] = True¶: Class-level flag indicating that the parameter estimator populates samples and costs directly from a multi-point evaluation (PosteriorResult for MCMC, EnsembleResult for grid/point/batch eval). Optimizer-style results leave this False and DataFit fills samples from the cost-logger history instead.

marginal(name: str) → ndarray¶: Flattened post-burnin samples for parameter name.

posterior_mean() → dict[str, float]¶: Mean of each parameter’s marginal.

property posterior_summary: dict[str, dict[str, float]] | None¶: Per-parameter {mean, std, median, q05, q95}.

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

property x¶: Lowest-cost sample row (parity with scipy OptimizeResult).

class ionworkspipeline.RegressionResult(parameter_values: dict | None = None, *, x: Any = None, results: Any = None, **kw)¶

DataFit return for Regressor optimizers.

Extends: ParameterEstimatorResult

to_config() → dict¶

Convert the parameter values to a JSON-serializable dictionary.

Optionally saves to a file.

Parameters¶

filenamestr, optional: The filename to save the JSON file to. If not provided, the dictionary is not saved.

Returns¶

dict: The JSON-serializable dictionary.

Examples¶

>>> param = pybamm.ParameterValues({"Temperature [K]": 298.15})
>>> param_dict = param.to_json()  # Get dictionary
>>> isinstance(param_dict, dict)
True
>>> param.to_json("parameters.json")
{'Temperature [K]': 298.15}

ionworkspipeline.data_fits.results.combine(children: list[ParameterEstimatorResult]) → ParameterEstimatorResult¶: Combine children into a single result (single-child returns as-is).