4.2

BayesQuasi v1

../_images/BayesQuasi-v1_dlg.png

BayesQuasi dialog.

Summary

This algorithm runs the Fortran QLines programs which fits a Delta function of amplitude 0 and Lorentzians of amplitude A(j) and HWHM W(j) where j=1,2,3. The whole function is then convolved with the resolution function.

Properties

Name Direction Type Default Description
Program Input string QL The type of program to run (either QL or QSe). Allowed values: [‘QL’, ‘QSe’]
SampleWorkspace Input MatrixWorkspace Mandatory Name of the Sample input Workspace
ResolutionWorkspace Input MatrixWorkspace Mandatory Name of the resolution input Workspace
ResNormWorkspace Input WorkspaceGroup   Name of the ResNorm input Workspace
MinRange Input number -0.2 The start of the fit range. Default=-0.2
MaxRange Input number 0.2 The end of the fit range. Default=0.2
SampleBins Input number 1 The number of sample bins
ResolutionBins Input number 1 The number of resolution bins
Elastic Input boolean True Fit option for using the elastic peak
Background Input string Flat Fit option for the type of background. Allowed values: [‘Sloping’, ‘Flat’, ‘Zero’]
FixedWidth Input boolean True Fit option for using FixedWidth
UseResNorm Input boolean False fit option for using ResNorm
WidthFile Input string   The name of the fixedWidth file
Loop Input boolean True Switch Sequential fit On/Off
OutputWorkspaceFit Output WorkspaceGroup Mandatory The name of the fit output workspaces
OutputWorkspaceResult Output MatrixWorkspace Mandatory The name of the result output workspaces
OutputWorkspaceProb Output MatrixWorkspace   The name of the probability output workspaces

Description

This algorithm can only be run on windows due to f2py support and the underlying fortran code

The model that is being fitted is that of a delta-function (elastic component) of amplitude A(0) and Lorentzians of amplitude A(j) and HWHM W(j) where j=1,2,3. The whole function is then convolved with the resolution function. The -function and Lorentzians are intrinsically normalised to unity so that the amplitudes represent their integrated areas.

For a Lorentzian, the Fourier transform does the conversion: 1/(x^{2}+\delta^{2}) \Leftrightarrow exp[-2\pi(\delta k)]. If x is identified with energy E and 2\pi k with t/\hbar where t is time then: 1/[E^{2}+(\hbar / \tau)^{2}] \Leftrightarrow exp[-t/\tau] and \sigma is identified with \hbar / \tau. The program estimates the quasielastic components of each of the groups of spectra and requires the resolution file and optionally the normalisation file created by ResNorm.

For a Stretched Exponential, the choice of several Lorentzians is replaced with a single function with the shape : \psi\beta(x) \Leftrightarrow exp[-2\pi(\sigma k)\beta]. This, in the energy to time FT transformation, is \psi\beta(E) \Leftrightarrow exp[-(t/\tau)\beta]. So \sigma is identified with (2\pi)\beta\hbar/\tau. The model that is fitted is that of an elastic component and the stretched exponential and the program gives the best estimate for the \beta parameter and the width for each group of spectra.

Usage

Example - BayesQuasi

# Check OS support for F2Py
from IndirectImport import is_supported_f2py_platform
if is_supported_f2py_platform():
    # Load in test data
    sampleWs = Load('irs26176_graphite002_red.nxs')
    resWs = Load('irs26173_graphite002_red.nxs')

    # Run BayesQuasi algorithm
    fit_ws, result_ws, prob_ws = BayesQuasi(Program='QL', SampleWorkspace=sampleWs, ResolutionWorkspace=resWs,
                                        MinRange=-0.547607, MaxRange=0.543216, SampleBins=1, ResolutionBins=1,
                                        Elastic=False, Background='Sloping', FixedWidth=False, UseResNorm=False,
                                        WidthFile='', Loop=True)

Categories: AlgorithmIndex | Workflow\MIDAS

Source

Python: BayesQuasi.py (last modified: 2019-02-26)