ess.snspowder.powgen.peaks.fit_vanadium_peaks#
- ess.snspowder.powgen.peaks.fit_vanadium_peaks(data, *, peak_estimates=None, windows=None, background=None, peak=None, fit_parameters=None, fit_requirements=None)[source]#
Fit coherent scattering peaks of vanadium.
This function wraps
scippneutron.peaks.fit_peaks()
and provides default parameters for vanadium at POWGEN.- Parameters:
data (
DataArray
) – A 1d data array wheredata.data
is the dependent variable anddata.coords[data.dim]
is the independent variable for the fit. Must be 1-dimensional and not binned.peak_estimates (
Optional
[Variable
], default:None
) – Initial estimates of peak locations. A peak will be fitted for each estimate. Must be a 1d variable with dimensiondata.dim
. IfNone
, estimates are derived usingtheoretical_vanadium_dspacing()
.windows (
Optional
[Variable
], default:None
) –If a scalar, the size of fit windows. A window is constructed for each peak estimate centered on the estimate with a width equal to
windows
(adjusted to the data range and to maintain a separation between peaks, seescippneutron.peaks.FitParameters.neighbor_separation_factor
).If a 2d array, the windows for each peak. Must have sizes
{data.dim: len(data), 'range': 2}
wherewindows['range', 0]
andwindows['range', 1]
are the lower and upper bounds of the fit windows, respectively. The windows are not adjusted automatically in this case.Defaults to
sc.scalar(0.02, unit='angstrom')
.background (
Union
[Model
,str
,Iterable
[Model
],Iterable
[str
],None
], default:None
) – The background model or models. Defaults to('linear', 'quadratic')
. That is, a fit with a linear background is attempted, and if the fit fails, a quadratic background is tried.peak (
Union
[Model
,str
,Iterable
[Model
],Iterable
[str
],None
], default:None
) – The peak model or models. Defaults to'gaussian'
.fit_parameters (
Optional
[FitParameters
], default:None
) – Parameters for the fit not otherwise listed as function arguments.fit_requirements (
Optional
[FitRequirements
], default:None
) – Constraints on the fit result.
- Returns:
list
[FitResult
] – AFitResult
for each peak.