.. algorithm:: .. summary:: .. relatedalgorithms:: .. properties:: Description ----------- 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: :math:`1/(x^{2}+\delta^{2}) \Leftrightarrow exp[-2\pi(\delta k)]`. If x is identified with energy E and :math:`2\pi k` with :math:`t/\hbar` where t is time then: :math:`1/[E^{2}+(\hbar / \tau)^{2}] \Leftrightarrow exp[-t/\tau]` and :math:`\sigma` is identified with :math:`\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 : :math:`\psi\beta(x) \Leftrightarrow exp[-2\pi(\sigma k)\beta]`. This, in the energy to time FT transformation, is :math:`\psi\beta(E) \Leftrightarrow exp[-(t/\tau)\beta]`. So :math:`\sigma` is identified with :math:`(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 :math:`\beta` parameter and the width for each group of spectra. Usage ----- **Example - BayesQuasi** .. testcode:: BayesQuasiExample # 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:: .. sourcelink::