mantidimaging.core.fitting.fitting_engine module

class mantidimaging.core.fitting.fitting_engine.FittingEngine(model: BaseFittingFunction)

Bases: object

find_best_fit(xdata: ndarray, ydata: ndarray, initial_params: list[float], params_bounds: list[tuple[float | None, float | None]] | None = None) → tuple[dict[str, float], float, float]

Fit the model to the given spectrum using unweighted least squares.

Returns:

• fit_params: dictionary of parameter names → fitted values

• rss: residual sum of squares (Σ(residual²))

• reduced_rss: rss / degrees of freedom, where DoF = N - p

Notes:

Uses the Nelder–Mead minimizer on the unweighted residuals.

get_init_params_from_roi(region: FittingRegion) → dict[str, float]

get_parameter_names() → list[str]

set_fitting_model(model: BaseFittingFunction) → None