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EnggEstimateFocussedBackground v1¶
Summary¶
Performs iterative smoothing (lowpass filter) to estimate the background of a spectrum
See Also¶
Properties¶
Name 
Direction 
Type 
Default 
Description 

InputWorkspace 
Input 
Mandatory 
Workspace with focussed spectra 

OutputWorkspace 
Output 
Mandatory 
Workspace to contain the estimated background (one for each spectrum in the InputWorkspace) 

NIterations 
Input 
number 
50 
Number of iterations of the smoothing procedure to perform. Too few iterations and the background will be enhanced in the peak regions. Too many iterations and the background will be unrealistically low and not catch the rising edge at low TOF/dspacing (typical values are in range 20100). 
XWindow 
Input 
number 
600 
Extent of the convolution window in the xaxis for all spectra. A reasonable value is about 48 times the FWHM of a typical peak/feature to be suppressed (default is reasonable for TOF spectra). This is converted to an odd number of points using the median bin width of each spectra. 
ApplyFilterSG 
Input 
boolean 
True 
Apply a Savitzky–Golay filter with a linear polynomial over the same XWindow before the iterative smoothing procedure (recommended for noisy data) 
Description¶
Estimates the background for a spectra by employing an iterative smoothing procedure adapted from an algorithm published in [1]. In each iteration a smoothing window is convolved with the data to produce a new spectra which is compared point by point with spectra from the previous iteration, with the lowest of the two intensities retained. In this way counting statistics are taken into conisderation to some extent, but if the data are noisy then this can lead to the background being underestimated (by the approxiumate amplitude of the noise). This can be avoided by rebinning/rebunching data, however this is not always desirable. Therefore there is an option to apply a Savitzky–Golay filter with a linear polynomial over a window the same length as the smoothing window before the iterative smoothing.
Useage¶
Example:
from mantid.simpleapi import *
CreateSampleWorkspace(OutputWorkspace='ws', Function='Multiple Peaks', NumBanks=1, BankPixelWidth=1, XMax=20, BinWidth=0.2)
EnggEstimateFocussedBackground(InputWorkspace='ws', OutputWorkspace='ws_bg', NIterations='200', XWindow=2.5, ApplyFilterSG=False)
References¶
The source for how this calculation is done is
Brückner, S. (2000). Estimation of the background in powder diffraction patterns through a robust smoothing procedure. Journal of Applied Crystallography, 33(3), 977979._
Categories: AlgorithmIndex  Diffraction\Engineering