Lorentzian1D

From MantidProject

Contents 1 Deprecation notice 2 Summary 3 Properties 4 Source Code

Deprecation notice

Instead of using this algorithm to fit a Lorentzian please use the Fit algorithm where the Function parameter of this algorithm is used to specified the fitting function, including selecting a Lorentzian.

Summary

Takes a histogram in a 2D workspace and fit it to a Lorentzian function, i.e. to the function:

where

• BG0 - constant background value
• BG1 - constant background value
• Height - height of peak (at maximum)
• PeakCentre - centre of peak
• HWHM - half-width at half-maximum

Note that the FWHM (Full Width Half Maximum) equals two times HWHM, and the integral over the Lorentzian equals Height * π * HWHM (ignoring the linear background). In the literature you may also often see the notation γ = HWHM.

The figure below illustrate this symmetric peakshape function fitted to a TOF peak:

Properties

Order	Name	Direction	Type	Default	Description
1	InputWorkspace	Input	Workspace	Mandatory	The name of the input Workspace
2	SpectrumIndex	Input	integer	0	The spectrum to fit, using the workspace numbering of the spectra (default 0)
3	StartX	Input	double	Lowest value of x data array	X value to start fitting from
4	EndX	Input	double	Highest value of x data array	last X value to include in fitting range
5	BG0	InOut	double	0.0	constant background value
6	BG1	InOut	double	0.0	linear background modelling parameter
7	Height	InOut	double	0.0	height of peak (not the height may be refined to a negative value to fit a dipped curve)
8	PeakCentre	InOut	double	0.0	Centre of peak
9	HWHM	InOut	double	1.0	half-width at half-maximum
10	MaxIterations	Input	Integer	500	Max iterations
11	Output Status	Output	String	""	whether the fit was successful
12	Output Chi^2/DoF	Output	double	0.0	returns how good the fit was

Source Code

Header Lorentzian1D.h

Source Lorentzian1D.cpp