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MatchSpectra v1

Summary

Calculate factors to most closely match all spectra to reference spectrum

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	Workspace to match the spectra between
OutputWorkspace	Output	MatrixWorkspace	Mandatory	Workspace with the spectra matched
ReferenceSpectrum	Input	long	1	Spectrum to match other spectra to
CalculateOffset	Input	boolean	True	Calculate vertical shift
CalculateScale	Input	boolean	True	Calculate scale factor
Offset	Output	dbl list		Additive factor from matching
Scale	Output	dbl list		Multiplicitive factor from matching
ChiSq	Output	dbl list		Unweighted ChiSq between the spectrum and the reference. NaN means that the spectrum was not matched

Description

Calculate the factor(s) that best match the individual spectra to a reference spectrum using Gauss-Markov process. This is the same algorithm used by the “blend” step of PDFgetN. The data undergoes the linear transformation

\[scale * y_{spectrum} + offset\]

such that the distance between \(y_{reference}\) and the transformed spectrum is minimized. Only the portions of the spectra where the spectra overlap with the same x-values and the uncertainties are greater than zero are considered.

Note

Gauss-Markov does not take into account varying uncertainties when calculating the scale and offset.

Usage

import numpy as np
x = np.arange(100, dtype=float)
x = np.tile(x, 2)
y = np.arange(100, dtype=float)
y = np.tile(y, 2)
y[100:200] += 10
dy = np.zeros(y.size, dtype=float) + 1.
CreateWorkspace(OutputWorkspace='MatchSpectra_input',
                DataX=x,
                DataY=y,
                DataE=dy,
                NSpec=2)
_, offset, scale, chisq = MatchSpectra(InputWorkspace='MatchSpectra_input',
                                       OutputWorkspace='MatchSpectra_output',
                                       ReferenceSpectrum=2,
                                       CalculateOffset=True,
                                       CalculateScale=False)
for i in range(2):
    print('spectra {}: {:.1f} * y + {:.1f}, chisq={:.1f}'.format(i+1, scale[i], offset[i], chisq[i]))

Output:

spectra 1: 1.0 * y + 10.0, chisq=0.0
spectra 2: 1.0 * y + 0.0, chisq=0.0

Categories: AlgorithmIndex | Diffraction\Reduction

Source

Python: MatchSpectra.py