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CentroidPeaksMD dialog.
Table of Contents
Find the centroid of single-crystal peaks in a MDEventWorkspace, in order to refine their positions.
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | MDEventWorkspace | Mandatory | An input MDEventWorkspace. |
| PeakRadius | Input | number | 1 | Fixed radius around each peak position in which to calculate the centroid. |
| PeaksWorkspace | Input | IPeaksWorkspace | Mandatory | A PeaksWorkspace containing the peaks to centroid. |
| OutputWorkspace | Output | IPeaksWorkspace | Mandatory | The output PeaksWorkspace will be a copy of the input PeaksWorkspace with the peaks’ positions modified by the new found centroids. |
This algorithm starts with a PeaksWorkspace containing the expected positions of peaks in reciprocal space. It calculates the centroid of the peak by calculating the average of the coordinates of all events within a given radius of the peak, weighted by the weight (signal) of the event.
Example - CentroidPeaksMD:
The code iteslef works but disabled from doc tests as takes too long to complete. User should provide its own event nexus file instead of TOPAZ_3132_event.nxs used within this example. The original TOPAZ_3132_event.nxs file is available in Mantid system tests repository.
The example shows how applying centroid peaks changes the peak posisions previously calculated by FindPeaksMD algorithm.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | #.. testcode:: exCentroidPeaksMD
def print_WSDifference(pTWS1,pTWS2,nRows):
''' Method to print difference between two table workspaces before and after applying CentroidPeaks '''
# columns to compare
tab_names=['RunNumber','DetID','Energy','DSpacing','QLab','QSample']
common = tab_names[0:2];
long = tab_names[-2:];
for name in tab_names:
if name in common :
print("| {0:>10} ".format(name))
else:
if name in long:
print("|FindPeaksMD found (old):{0:>7} |IntegrEllipsoids (new): {0:>7} ".format(name))
else:
ntp = name;
if len(ntp )>6:
ntp = ntp[0:6]
print("| old {0:>6} | new {0:>6} ".format(ntp))
print("|\n" )
for i in xrange(0,nRows):
for name in tab_names:
col1 = pTWS1.column(name);
data1_toPr=col1[i]
col2 = pTWS2.column(name);
data2_toPr=col2[i]
if name in common :
print("| {0:>10} ".format(data1_toPr))
else:
if name in long:
print("| {0:>30} | {1:>30} ".format(data1_toPr,data2_toPr))
else:
print("| {0:>10.2f} | {1:>10.2f} ".format(data1_toPr,data2_toPr))
print("|\n ")
# load test workspace
Load(Filename=r'TOPAZ_3132_event.nxs',OutputWorkspace='TOPAZ_3132_event',LoadMonitors='1')
# build peak workspace necessary for IntegrateEllipsoids algorithm to work
ConvertToMD(InputWorkspace='TOPAZ_3132_event',QDimensions='Q3D',dEAnalysisMode='Elastic',Q3DFrames='Q_sample',LorentzCorrection='1',OutputWorkspace='TOPAZ_3132_md',\
MinValues='-25,-25,-25',MaxValues='25,25,25',SplitInto='2',SplitThreshold='50',MaxRecursionDepth='13',MinRecursionDepth='7')
# get initial peak workspace
peaks=FindPeaksMD(InputWorkspace='TOPAZ_3132_md',PeakDistanceThreshold='0.37680',MaxPeaks='50',DensityThresholdFactor='100',OutputWorkspace='TOPAZ_3132_peaks')
# refine peaks position with centroid peaks
peaks2=CentroidPeaksMD(InputWorkspace='TOPAZ_3132_md', PeaksWorkspace='TOPAZ_3132_peaks', OutputWorkspace='TOPAZ_3132_peaks2')
print_WSDifference(peaks,peaks2,10)
**Output:**
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | #.. testoutput:: exCentroidPeaksMD
| RunNumber | DetID | old Energy | new Energy | old DSpaci | new DSpaci |FindPeaksMD found (old): QLab |IntegrEllipsoids (new): QLab |FindPeaksMD found (old):QSample |IntegrEllipsoids (new): QSample |
| 3132 | 1124984 | 8.49 | 10.39 | 2.02 | 1.93 | [1.57771,1.21779,2.37854] | [1.9157,1.15022,2.37669] | [2.99396,0.815958,0.00317344] | [3.13041,0.861402,0.316416] |
| 3132 | 1156753 | 18.82 | 18.87 | 1.30 | 1.29 | [2.48964,1.45725,3.88666] | [2.50792,1.41823,3.91448] | [4.52618,1.71025,0.129461] | [4.52916,1.75746,0.149293] |
| 3132 | 1141777 | 28.09 | 29.63 | 1.05 | 1.04 | [2.60836,2.31423,4.86391] | [2.9387,2.15218,4.7974] | [5.69122,1.79492,-0.452799] | [5.72802,1.86148,-0.0867018] |
| 3132 | 1125241 | 33.86 | 32.09 | 1.01 | 1.01 | [3.15504,2.42573,4.75121] | [3.12135,2.20547,4.87426] | [5.97829,1.63473,0.0118744] | [5.9025,1.87759,0.0200907] |
| 3132 | 1170598 | 34.12 | 32.63 | 0.95 | 0.96 | [3.43363,1.70178,5.39301] | [3.2557,1.75038,5.41104] | [6.07726,2.59962,0.281759] | [6.02352,2.57854,0.105647] |
| 3132 | 1214951 | 22.79 | 19.55 | 1.68 | 1.67 | [2.73683,1.43808,2.11574] | [2.60506,1.43592,2.30563] | [3.5786,0.470838,1.00329] | [3.62222,0.607039,0.821705] |
| 3132 | 1207827 | 27.89 | 29.54 | 1.32 | 1.31 | [2.80324,2.29519,3.09134] | [2.99683,2.18047,3.05302] | [4.71517,0.554412,0.37714] | [4.72528,0.607846,0.598834] |
| 3132 | 1232949 | 53.28 | 57.02 | 0.93 | 0.93 | [4.29033,2.63319,4.46168] | [4.40869,2.69431,4.34027] | [6.52658,1.27985,1.00646] | [6.5525,1.15043,1.12919] |
| 3132 | 1189484 | 63.42 | 60.85 | 0.96 | 0.96 | [4.02414,3.39659,3.83664] | [4.15914,3.15181,3.95843] | [6.4679,0.298896,0.726133] | [6.46553,0.557683,0.887368] |
| 3132 | 1218337 | 79.81 | 87.16 | 0.77 | 0.77 | [4.96622,3.61607,5.32554] | [5.17998,3.67105,5.16175] | [7.99244,1.19363,0.892655] | [8.03942,1.03829,1.11448] |
|
Categories: AlgorithmIndex | MDAlgorithms\Peaks