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MatchSpectra v1

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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