FilterClusterFaces enhancements

[Owen Arnold]

• If FilteringWorkspace is provided, then output an additional column with the radius of the face from the peak centre. This will allow client code to determine an effective radius of each cluster
• Check that the MDHistoWorkspace provided actually contains labels ids rather than being a generic MDHistoWorkspace. I think the algorithm would probably badly misbehave if the latter case was currently provided. To check. For each signal value perform signal_value%1 == 0.
• Parallelise the algorithm according to the criteria already defined in the algorithm comments. As an intermediate data structure, TableWorkspaces themselves may work best.
• Add the ability to limit the number of output rows, and report that truncation has occurred in that eventuality.

[Owen Arnold]

For the performance side of things - current timings on my Release Windows 7 build are:

test_execution_unfiltered = 0.015 seconds

test_execution_filtered = 0.018 seconds

[Owen Arnold]

refs #9455. Create and use intermediate structure

This is easy and safe to do, because unit tests will pick up any changes in the final output TableWorkspace.

Changeset: 989d5e068e0b67a2f97afd8e95fe41bdec151e95

[Owen Arnold]

refs #9455. Break jobs into distributed parts.

This is not yet executing in the context of an openMP loop, but the working is now fully separated, and should be thread-safe.

Changeset: eb592e4edfd2dc0c512a59791aa5c81d02796b06

[Owen Arnold]

refs #9455. Run jobs in parallel.

Changeset: 5ba3891cdb71d7cce2dbff1d90bf5cede6a2734b

[Owen Arnold]

The refactoring to distributable execution step did not slow the processing down by any noticeable amount. I expect that it would slow it down given that the rows could not be written until the end. The last stage where the open mp loop was switched on resulted in a good speed improvement.

test_execution_unfiltered = 0.008 seconds

test_execution_filtered = 0.009 seconds

[Owen Arnold]

refs #9455. Add progress reporting.

Changeset: 62486340c7210f7f137ca831a3f8a7f2a0c63fc9

[Owen Arnold]

refs #9455. Add signal value checking.

Add tests for this too.

Changeset: 49a64918a64edc9bbb3241425e1287b62e6a9ebd

[Owen Arnold]

refs #9455. Add radius calculation.

The code is functionally correct, as the tests show, but the implementation is not good. Needs some refactoring work before this is complete.

Changeset: 98a5b8a27b861b6c82db43568bb263f6a2f0111c

[Owen Arnold]

refs #9455. Refactor to separate processing.

Refactor to separate out unfiltered from filtered processing. Code duplication still needs to be addressed.

Changeset: 927ae6c47c5904aebf86af83ed853a5e283c021e

[Owen Arnold]

refs #9455. Refactoring to achieve better code reuse.

Changeset: b0ef8e344b472ab1b2eadc402ae82f30f4ae3f29

[Owen Arnold]

refs #9455. Add row limit features.

Fully tested functionality provided.

Changeset: 7593279b23fe2b6adfa02ba7a41e7e17200a321b

[Owen Arnold]

refs #9455. Fix warning.

Changeset: bee839acc5e0d774f3d1ebf2c21e4a84261d7348

[Owen Arnold]

refs #9455. Remove qualifier.

Changeset: fe2552e74e9385337e0b1db01b2dae52970f812e

[Owen Arnold]

refs #9455. Fix type conversion warnings.

Changeset: 6eb03a2453201f32ae176a62e356c89646cf9516

[Owen Arnold]

refs #9455. All column names uppercase.

Changeset: 9901b25fc3709cba3c927a91e209236efcfb70b6

[Owen Arnold]

Tester:

There are quite a few changes here. All changes are either covered by functional unit tests or performance tests.

To test the new radius functionality. Run this script. You should see that the main cluster has the same radius extents (in the SliceViewer) as reported at the end of the script.

ws_name = "TOPAZ_3132"
filename = ws_name +"_event.nxs"
ws = LoadEventNexus(Filename=filename,FilterByTofMin=3000, FilterByTofMax=16000)

# Convert to Q space
LabQ = ConvertToDiffractionMDWorkspace(InputWorkspace=ws, LorentzCorrection='0',
        OutputDimensions='Q (lab frame)', SplitInto=2, SplitThreshold=150)
        
# Find peaks
PeaksLattice = FindPeaksMD(InputWorkspace=LabQ,MaxPeaks=100)

        
# Find the UB matrix using the peaks and known lattice parameters
FindUBUsingLatticeParameters(PeaksWorkspace=PeaksLattice, a=10.3522, b=6.0768, c=4.7276,
                alpha=90, beta=90, gamma=90, NumInitial=20, Tolerance=0.12)
                
# And index to HKL            
IndexPeaks(PeaksWorkspace=PeaksLattice, Tolerance=0.12)

HistoMDQLab = BinMD(InputWorkspace=LabQ,AlignedDim0='Q_lab_x, 2.7, 3, 50',AlignedDim1='Q_lab_y, -0.9, -0.6, 50',AlignedDim2='Q_lab_z, 9.2, 9.5,  50')   
PeaksLattice_Integrated_Clusters, ClusterImage = IntegratePeaksUsingClusters(InputWorkspace=HistoMDQLab, PeaksWorkspace=PeaksLattice, Threshold=100000)

# Show Cluster with Peak Overlayed. Only One point where a PeaksWorkspace intersects with a Cluster!
svw = plotSlice(ClusterImage, xydim=['Q_lab_x', 'Q_lab_z'])
sv = svw.getSlicer()
pp_all = sv.setPeaksWorkspaces([PeaksLattice_Integrated_Clusters])
pp = pp_all.getPeaksPresenter(PeaksLattice_Integrated_Clusters)
pp.zoomToPeak(30)
svw.zoomBy(0.03)

# So finding with the PeaksWorkspace as a filter should yield results that have the SAME value for 'ClusterId 'in all rows!
with_filter, truncated = FindClusterFaces(InputWOrkspace=ClusterImage, FilterWorkspace=PeaksLattice)

def max_radius(t):
        max = -0.0
        for i in range(t.rowCount()):
                _row =  t.row(i)
                current_radius = _row['Radius'] 
                if  current_radius > max:
                        max = current_radius
        return max
        

radius_estimate =  max_radius(with_filter)
print "Radius estimate for the cluster is : ", radius_estimate

[Owen Arnold]

Merge remote-tracking branch 'origin/feature/9455_cluster_faces_enhance'

Full changeset: ​4f5df39b581350d6b6f931b15fc60608f621fbd1

[Stuart Campbell]

This ticket has been transferred to github issue ​10298