FindPeakBackground dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | MatrixWorkspace | Mandatory | Name of input MatrixWorkspace that contains peaks. |
| WorkspaceIndex | Input | number | Optional | workspace indices to have peak and background separated. No default is taken. |
| SigmaConstant | Input | number | 1 | Multiplier of standard deviations of the variance for convergence of peak elimination. Default is 1.0. |
| FitWindow | Input | dbl list | Optional: enter a comma-separated list of the minimum and maximum X-positions of window to fit. The window is the same for all indices in workspace. The length must be exactly two. | |
| BackgroundType | Input | string | Linear | Type of Background. Allowed values: [‘Flat’, ‘Linear’, ‘Quadratic’] |
| OutputWorkspace | Output | TableWorkspace | Mandatory | The name of the TableWorkspace in which to store the background found for each index. Table contains the indices of the beginning and ending of peak and the estimated background coefficients for the constant, linear, and quadratic terms. |
Algorithm written using the paper referenced below which has a very good description.
This algorithm estimates the background level and separates the background from signal data in a Poisson-distributed data set by statistical analysis. For each iteration, the bins/points with the highest intensity value are eliminated from the data set and the sample mean and the unbiased variance estimator are calculated. Convergence is reached when the absolute difference between the sample mean and the sample variance of the data set is within k standard deviations of the variance, the default value of k being 1. The k value is called SigmaConstant in the algorithm input.
Objective algorithm to separate signal from noise in a Poisson-distributed pixel data set by T.Straasø.., D. Mueter, H. O.Sørensen..and J. Als-Nielsen Strass J. Appl. Cryst. (2013). 46, 663-671
Categories: Algorithms | Utility