ewoksxas.fit.peak_fit.MultiPeakFitResult#
- class ewoksxas.fit.peak_fit.MultiPeakFitResult(manager, shape, roi)[source]#
Bases:
objectThe result of an N-peak fit, exposing per-peak physical descriptors.
The bare scalar properties (
center,area, …) report the first (leftmost) peak so that single-peak callers keep working unchanged; the full per-peak descriptors are available throughpeaks.- property area: float#
Integrated area of the first peak.
- as_dict()[source]#
Return the first peak’s descriptors and the fit quality as a dict.
- Return type:
dict[str,float]
- property center: float#
Center x0 of the first (leftmost) peak.
- property dof: int#
Degrees of freedom of the fit (data points minus varied params).
- property eta: float#
Pseudo-Voigt mixing fraction of the first peak (Voigt shapes only).
- property fwhm: float#
Total full width at half maximum of the first (symmetric) peak.
- property fwhm_high: float#
High-side full width at half maximum of the first (asymmetric) peak.
- property fwhm_low: float#
Low-side full width at half maximum of the first (asymmetric) peak.
- property height: float#
Maximum height of the first peak above the baseline.
- property n_peaks: int#
Number of fitted peaks.
- property peaks: list[dict[str, float]]#
Per-peak descriptor dictionaries, ordered by ascending center.
- property r2: float#
Coefficient of determination of the total model.
- property rmse: float#
Root-mean-square error of the total model against the data.
- without_negligible_peaks()[source]#
Return a result with vanishing peaks dropped (for automatic counting).
silx may over-detect peaks; a surplus peak’s height and area are driven towards zero by the fit. Those are removed so the output reports only the peaks the data supports. A zeroed peak’s removal barely perturbs the model curve, so no re-fit is needed; at least the tallest peak is kept, and
selfis returned when nothing is pruned.- Return type: