GetDetOffsetsMultiPeaks v1¶

GetDetOffsetsMultiPeaks dialog.

Table of Contents

Summary ¶

Creates an OffsetsWorkspace containing offsets for each detector. You can then save these to a .cal file using SaveCalFile.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	A 2D matrix workspace with X values of d-spacing
DReference	Input	dbl list		Enter a comma-separated list of the expected X-position of the centre of the peaks. Only peaks near these positions will be fitted.
FitWindowMaxWidth	Input	number	0	Optional: The maximum width of the fitting window. If this is <=0 the window is not specified to FindPeaks
FitwindowTableWorkspace	Input	TableWorkspace		Name of the input Tableworkspace containing peak fit window information for each spectrum.
PeakFunction	Input	string	Gaussian	Type of peak to fit. Allowed values: [‘BackToBackExponential’, ‘Bk2BkExpConvPV’, ‘DeltaFunction’, ‘ElasticDiffRotDiscreteCircle’, ‘ElasticDiffSphere’, ‘ElasticIsoRotDiff’, ‘ExamplePeakFunction’, ‘Gaussian’, ‘IkedaCarpenterPV’, ‘Lorentzian’, ‘PseudoVoigt’, ‘Voigt’]
BackgroundType	Input	string	Linear	Type of Background. The choice can be either Linear or Quadratic. Allowed values: [‘Flat’, ‘Linear’, ‘Quadratic’]
HighBackground	Input	boolean	True	Relatively weak peak in high background
GroupingFileName	Input	string		Optional: The name of the output CalFile to save the generated OffsetsWorkspace. Allowed extensions: [‘.cal’]
OutputWorkspace	Output	OffsetsWorkspace		An output workspace containing the offsets.
NumberPeaksWorkspace	Output	OffsetsWorkspace		An output workspace containing the offsets.
MaskWorkspace	Output	MatrixWorkspace	Mask	An output workspace containing the mask.
MaxOffset	Input	number	1	Maximum absolute value of offsets; default is 1
MaxChiSq	Input	number	100	Maximum chisq value for individual peak fit allowed. (Default: 100)
MinimumPeakHeight	Input	number	2	Minimum value allowed for peak height.
MinimumPeakHeightObs	Input	number	0	Least value of the maximum observed Y value of a peak within specified region. If any peak’s maximum observed Y value is smaller, then this peak will not be fit. It is designed for EventWorkspace with integer counts.
Minimizer	Input	string	Levenberg-MarquardtMD	Minimizer to use for fitting peaks. Allowed values: [‘BFGS’, ‘Conjugate gradient (Fletcher-Reeves imp.)’, ‘Conjugate gradient (Polak-Ribiere imp.)’, ‘Damped GaussNewton’, ‘FABADA’, ‘Levenberg-Marquardt’, ‘Levenberg-MarquardtMD’, ‘Simplex’, ‘SteepestDescent’, ‘Trust Region’]
InputResolutionWorkspace	Input	MatrixWorkspace		Name of the optional input resolution (delta(d)/d) workspace.
SpectraFitInfoTableWorkspace	Output	TableWorkspace		Name of the output table workspace containing spectra peak fit information.
PeaksOffsetTableWorkspace	Output	TableWorkspace		Name of an output table workspace containing peaks’ offset data.
FittedResolutionWorkspace	Output	MatrixWorkspace	ResolutionWS	Name of the resolution workspace containing delta(d)/d for each unmasked spectrum.
MinimumResolutionFactor	Input	number	0.1	Factor of the minimum allowed Delta(d)/d of any peak to its suggested Delta(d)/d.
MaximumResolutionFactor	Input	number	10	Factor of the maximum allowed Delta(d)/d of any peak to its suggested Delta(d)/d.

This algorithm requires a workspace that is in d-spacingand has been rebinned to a common set of d-spacing bin boundaries. In this first step you select one spectrum to be the reference spectrum and all of the other spectrum are cross correlated against it. Each output spectrum then contains a peak whose location defines the offset from the reference spectrum.

The algorithm iterates over each spectrum in the workspace and fits a function (default is a Gaussian) to the reference peak. The fit is used to calculate the centre of the fitted peak, and the offset is then calculated as:

This is then written into a .cal file for every detector that contributes to that spectrum. All of the entries in the cal file are initially set to both be included, but also to all group into a single group on DiffractionFocussing v2. The CreateCalFileByNames v1 algorithm can be used to alter the grouping in the cal file.

Fit for peak offset ¶

The algorithm to calculate offset of peaks’ positions is to minimize a cost function as

$\sum_{p} |X_{0, p} - (1+offset)\cdot X_{0, p}|/\chi^2_{p}$

, which p is the index of a peak whose position is within MinD and MaxD.

Criteria on peaks ¶

The (fitted) peak must meet a series of criteria to be used to fit spectrum’s offset.

A peak will not be used if

its centre is out of pre-defined d-range, i.e., MinD and MaxD;
its centre is out of fitting window if it is defined;
its $\chi^2$ of peak fitting is larger than pre-defined maximum value;
its height is lower than pre-defined lowest peak height;
its observed maximum value corrected by background is smaller than user specified value;
its signal/noise ratio is less than 5 $H\cdot FWHM\_To\_SIGMA/width < 5$ ;
its height is not outside of error bars of background $H < \sqrt{H + B}/2$ ;
its z-value on $\frac{\delta d}{d}$ is larger than 2.0;
its offset from theoretical position exceeds the limitation specified by user;
its resolution ( $\Delta(d)/d$ ) is out of user-specified range.

Generate fit window ¶

There are two approach to generate fit window. One is via property ‘FitWindowMaxWidth’; and the other is ‘FitwindowTableWorkspace’.

If neither of these 2 properties are correctly specified, then then there won’t be any window defined.

Uniform fit window ¶

By specifying a positive float, maxWidth, for ‘FitWindowMaxWidth’, it is the definition of fit window for peaks indexed from 0 to N-1:

Peak 0: window = $\min((X0_0-dmin), maxWidth)$ , $\min((X0_1-X0_0)/2,maxWidth)$

Peak $i (0 < i < N-1)$ : window = $\min((X0_i-X0_{i-1})/2, maxWidth)$ , $\min((X0_1-X0_0)/2, maxWidth)$

Peak $N-1$ : window = $\min((X0_i-X0_{i-1})/2, maxWidth)$ , $\min((dmax-X0_i), maxWidth)$

where $X0_i$ is the centre of i-th peak.

Fit window for individual peak ¶

FitwindowTableWorkspace contains the fit window for each individual peak in the workspace to find. It contains $1+2\times N$ columns, where N is the number of peaks positions specified in ‘DReference’.

Column 0: spectrum number $spNum$ . If $spNum < 0$ , then it is a ‘universal’ spectrum;
Column $2i+1$ : left boundary of peak $i$ defined in ‘DReference’ of spectrum $iws$ ;
Column $2i+2$ : right boundary of peak $i$ defined in ‘DReference’ of spectrum $iws$ ;

Default fit windows ¶

In the fit window table workspace, if there is a row, whose ‘spectrum number’ is a negative number, then the fit windows defined in this row is treated as the default fit windows. It means that for any spectrum that has no fit windows defined in the tableworkspace, the default fit windows will be applied to it.

Quality of Fitting ¶

GetDetOffsetsMultiPeaks have 2 levels of fitting. First it will call FindPeaks to fit Bragg peaks within d-range. Then it will fit offsets from the peak positions obtained in the previous step. Therefore, the performance of FindPeaks is critical to this algorithm. It is necessary to output values reflecting the goodness of fitting of this algorithm to users.

Number of spectra that are NOT masked ¶

A spectrum will be masked if it is a dead pixel, has an empty detector or has no peak that can be fit with given peak positions. The performance of FindPeaks affects the third criteria. A better algorithm to find and fit peaks may save some spectrum with relatively much fewer events received, i.e., poorer signal.

$\chi^2$ of the offset fitting function ¶

The goodness of fit, $\chi^2_{spNum}$ , of the offset fitting function

$\sum_{p} |X_{0, p} - (1+offset)X_{0, p}|\cdot H^2_{p}$

is an important measure of fitting quality on each spectrum (indexed as spNum).

Deviation of highest peaks ¶

We observed that in some situation, the calibrated peaks’ positions of some spectra are far off to the targeted peak positions, while goodness of fit such as $\chi^2$ are still good. It is usually caused by the bad fit of one or two peaks in that spectrum, which feeds some erroreous peak positions to peak offset fitting function.

This type of bad fitting is very easily identified by visualization, because the shift of peaks from the correct positions is significant in fill plot.

Therefore, deviation of highest peak if spectrum i, $D_{i}$ is defined as:

$D_{i} = |X^{(o)}\cdots(1+offset) - X^{(c)}|$

where $X^{(o)}$ is the fitted centre of the highest peak of spectrum i, and $X^{(c)}$ is the theoretical centre of this peak.

Collective quantities to illustrate goodness of fitting (still in development)¶

Be noticed that the idea of this section is still under development and has not been implemented yet.

On the other hand, since GetDetOffsetsMultiPeaks always operates on an EventWorkspace with thousands or several ten thousands of spectra, it is very hard to tell the quality of fitting by looking at $\chi^2_{spNum}$ of all spectra. Hence, Here are two other parameters are defined for comparison of results.

$g_1 = \frac{\sum_{s}D_{s}^2}{N_{nm}}$

, where s is the index of any unmasked spectrum and $N_{mn}$ is the number of unmasked spectra;

$g_2 = \frac{\sum_{s}D_{s}^2\cdot H_{s}^2}{N_{nm}}$ ,

where $H_{s}$ is the height of highest peak of spectrum s.

Standard error on offset ¶

The offset in unit of d-spacing differs is proportional to peak’s position by definition:

$X_0^{(f)} = X_0^{(o)} * (1+offset)$

where $X_0^{(f)}$ is the focussed peak position, and $X_0^{(o)}$ is the observed peak position by fitting.

As different spectrum covers different d-space range, the highest peak differs. Therefore, the error of offset should be normalized by the peak’s position.

$E = (X_0^{(f)} - X_0^{(o)}*(1+offset))/X_0^{(f)} = 1 - \frac{X_0^{(o)}}{X_0^{(f)}}\cdot(1+offset)$

And it is unitless.

By this mean, the error of all peaks should be close if they are fitted correctly.

Spectra to be masked ¶

A MaskWorskpace is output from the algorithm. Along with it, a TableWorkspace is output to describe the status of offset calculation.

Here are the cases that a spectra (i.e., a detector) will be masked in the output MaskWorkspace.

An empty spectrum (i.e., the corresponding EventList is empty). It is noted as “empty det”;
A dead detector, i.e., the corresponding spectrum has counts less than $10^{-3}$ in defined d-range. It isnoted as “dead det”;
A spectrum that does not have peak within specified d-range. It is noted as “no peaks”. Here is the criteria for this case.

Algorithm FindPeaks fails to find any peak;

No peak found has height larger than specified ‘MinimumPeakHeight’;

No peak found has observed height larger than specified ‘MinimumPeakHeightObs’;

No peak found has resolution within specified range;

No peak found whose calculated offset is smaller than the user-defined maximum offset.

Usage ¶

import os

# Create a workspace with two Gaussian peaks in each spectrum
function_str = 'name=Gaussian,Height=3,PeakCentre=5,Sigma=0.3;name=Gaussian,Height=2.1,PeakCentre=15,Sigma=0.3'
ws = CreateSampleWorkspace(Function='User Defined',UserDefinedFunction=function_str,XMin=0,XMax=20,BinWidth=0.1)
# Make sure the X axis is in d-spacing.
ws.getAxis(0).setUnit( 'dSpacing' )

# Generate a file path to save the .cal file at.
calFilePath = os.path.expanduser( '~/MantidUsageExample_CalFile.cal' )

# Run the algorithm
msk = GetDetOffsetsMultiPeaks(ws,DReference=[5,15], GroupingFileName=calFilePath)

# Read the saved .cal file back in
f = open( calFilePath, 'r' )
file = f.read().split('\n')
f.close()

# Print out first 10 lines of the file
print("{} ...".format(file[0][:55]))
for line in file[1:10]:
  print(line)

Output ¶

# Calibration file for instrument basic_rect written on ...
# Format: number    UDET         offset    select    group
        0            100     -0.0033750       1       1
        1            101     -0.0033750       1       1
        2            102     -0.0033750       1       1
        3            103     -0.0033750       1       1
        4            104     -0.0033750       1       1
        5            105     -0.0033750       1       1
        6            106     -0.0033750       1       1
        7            107     -0.0033750       1       1

Source ¶

C++ source: GetDetOffsetsMultiPeaks.cpp (last modified: 2019-07-17)

C++ header: GetDetOffsetsMultiPeaks.h (last modified: 2018-10-05)

GetDetOffsetsMultiPeaks v1¶

Summary ¶

See Also ¶

Properties ¶

Description ¶

Fit for peak offset ¶

Criteria on peaks ¶

Generate fit window ¶

Uniform fit window ¶

Fit window for individual peak ¶

Default fit windows ¶

Quality of Fitting ¶

Number of spectra that are NOT masked ¶

$\chi^2$ of the offset fitting function ¶

Deviation of highest peaks ¶

Collective quantities to illustrate goodness of fitting (still in development)¶

Standard error on offset ¶

Spectra to be masked ¶

Usage ¶

Output ¶

Source ¶