autoemx.config.schema_models.fitting module

class autoemx.config.schema_models.fitting.FittedPeakResult(*, element: str, line: str, area: float | None = None, sigma: float | None = None, center: float | None = None, fwhm: float | None = None, peak_intensity: float | None = None, background_intensity: float | None = None, theoretical_energy: float | None = None, height: float | None = None, pb_ratio: float | None = None)

Bases: BaseModel

Persisted fit summary for one fitted elemental line.

element: str

line: str

area: float | None

sigma: float | None

center: float | None

fwhm: float | None

peak_intensity: float | None

background_intensity: float | None

theoretical_energy: float | None

height: float | None

pb_ratio: float | None

model_config = {'extra': 'forbid'}

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

classmethod validate_non_empty_strings(v: str) → str

classmethod validate_numeric_fields(v: float | None) → float | None

class autoemx.config.schema_models.fitting.FitResult(*, r_squared: float | None = None, reduced_chi_squared: float | None = None, fitted_peaks: Dict[str, ~autoemx.config.schema_models.fitting.FittedPeakResult]=<factory>)

Bases: BaseModel

Compact fit data persisted for downstream re-use without refitting.

r_squared: float | None

reduced_chi_squared: float | None

fitted_peaks: Dict[str, FittedPeakResult]

model_config = {'extra': 'forbid'}

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

classmethod validate_r_squared(v: float | None) → float | None

classmethod validate_reduced_chi_squared(v: float | None) → float | None