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Persistence Clustering 2

Persistence Clustering 2 Image

Pipeline description

This pipeline is similar to the previous examples of persistence clustering and performs a clustering by persistence on a 2D data set taken from the scikit-learn examples. Please check out the Karhunen-Love Digits 64-Dimensions example for an application of this pipeline on a real-life data set..

First, this example loads a point cloud from disk (top left view in the above screenshot), then it computes a mesh on which a density field is obtained with a Gaussian Resampling on the points (top right view in the above screenshot). This density field will be considered as the input scalar data.

Next, a PersistenceDiagram is computed and thresholds are applied base on persistence to maintain only the features with a persistence above a certain value. The result is a simplified persistence diagram (bottom left view in the above screenshot).

The simplified persistence diagram is then used as a constraint for the TopologicalSimplification of the input scalar data, giving us a simplified data.

From there a MorseSmaleComplex is computed (bottom right view in the above screenshot). Finally, by using the identifier of the 2-dimension cell of the Morse Smale complex where one point lands, a cluster identifier, encoded in the AscendingManifold field in the ouput, is given to it.

ParaView

To reproduce the above screenshot, go to your ttk-data directory and enter the following command: 

$ paraview --state=states/persistenceClustering2.pvsm

Python code

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from paraview.simple import *

# paraview 5.9 VS 5.10 compatibility ===========================================
def ThresholdBetween(threshold, lower, upper):
    try:
        # paraview 5.9
        threshold.ThresholdRange = [lower, upper]
    except:
        # paraview 5.10
        threshold.ThresholdMethod = "Between"
        threshold.LowerThreshold = lower
        threshold.UpperThreshold = upper
# end of comphatibility ========================================================

# create a new 'CSV Reader'
clusteringcsv = CSVReader(FileName=['clustering2.csv'])

# create a new 'Table To Points'
tableToPoints1 = TableToPoints(Input=clusteringcsv)
tableToPoints1.XColumn = 'X'
tableToPoints1.YColumn = 'Y'
tableToPoints1.a2DPoints = 1
tableToPoints1.KeepAllDataArrays = 1

# create a new 'Gaussian Resampling'
gaussianResampling1 = GaussianResampling(Input=tableToPoints1)
gaussianResampling1.ResampleField = ['POINTS', 'ignore arrays']
gaussianResampling1.ResamplingGrid = [256, 256, 3]
gaussianResampling1.GaussianSplatRadius = 0.05
gaussianResampling1.SplatAccumulationMode = 'Sum'

# create a new 'Slice'
slice1 = Slice(Input=gaussianResampling1)
slice1.SliceType = 'Plane'

# init the 'Plane' selected for 'SliceType'
slice1.SliceType.Normal = [0.0, 0.0, 1.0]

# create a new 'TTK PersistenceDiagram'
tTKPersistenceDiagram1 = TTKPersistenceDiagram(Input=slice1)
tTKPersistenceDiagram1.ScalarField = ['POINTS', 'SplatterValues']

# create a new 'Threshold'
threshold1 = Threshold(Input=tTKPersistenceDiagram1)
threshold1.Scalars = ['CELLS', 'PairIdentifier']
ThresholdBetween(threshold1, -0.1, 999999999)

# create a new 'Threshold'
persistenceThreshold0 = Threshold(Input=threshold1)
persistenceThreshold0.Scalars = ['CELLS', 'Persistence']
ThresholdBetween(persistenceThreshold0, 5.0, 999999999)

# create a new 'TTK TopologicalSimplification'
tTKTopologicalSimplification1 = TTKTopologicalSimplification(Domain=slice1,
    Constraints=persistenceThreshold0
)
tTKTopologicalSimplification1.ScalarField = ['POINTS', 'SplatterValues']

# create a new 'TTK MorseSmaleComplex'
tTKMorseSmaleComplex1 = TTKMorseSmaleComplex(Input=tTKTopologicalSimplification1)
tTKMorseSmaleComplex1.ScalarField = ['POINTS', 'SplatterValues']

# create a new 'Resample With Dataset'
resampleWithDataset1 = ResampleWithDataset(SourceDataArrays=OutputPort(tTKMorseSmaleComplex1,3),
    DestinationMesh=tableToPoints1
)

# save the output(s)
SaveData('OutputClustering.csv', resampleWithDataset1)

Inputs

Outputs

  • data2Resampled.csv: the output is the data resampled in CSV file format, the cluster identifier of a point is given in the AscendingManifold field.

C++/Python API

PersistenceDiagram

TopologicalSimplification

MorseSmaleComplex