def getLowDimensionalSegments(highDimensionalData,n_components=2,plt=False,title="Latent space segments"): (lowDimensionalData,explainedVariance) = pca.pca(highDimensionalData,n_components) (mins,maxs) = segment.segmentationPoints(lowDimensionalData[:,0]) segments = pl.split(lowDimensionalData,maxs)[1:-1] if plt: plot.plotGridOf2Ds(segments,title) return (segments,explainedVariance)
pylab.figure(figsize=(12,9))
pylab.plot(d)
pylab.xlabel('Time (frames)')
pylab.ylabel('Sensor readings')
pylab.title('Raw data',fontsize=50)
pylab.tight_layout()
pylab.savefig('/Users/robertevans/repos/minf/Appendix/%s.png'%n, format='png', dpi=300)
for d,n in zip(data,pcaNames):
pylab.close()
pylab.figure(figsize=(12,9))
pylab.plot(pca.pca(d)[0])
pylab.xlabel('Time (frames)')
pylab.ylabel('Latent value')
pylab.title('Three Principal Components',fontsize=50)
pylab.tight_layout()
pylab.savefig('/Users/robertevans/repos/minf/Appendix/%s.png'%n, format='png', dpi=300)
for d,n in zip(data,segsNames):
pylab.close()
P = pca.pca(d)[0][:,0]
(mins,maxs) = segment.segmentationPoints(P, windowSize=100)
pylab.figure(figsize=(12,9))
pylab.plot(P)
for m in maxs:
pylab.axvline(m,color='r',linewidth=1.5)
pylab.xlabel('Time (frames)')
pylab.ylabel('Latent value')
pylab.title('Automatic segmentation',fontsize=50)
pylab.tight_layout()
pylab.savefig('/Users/robertevans/repos/minf/Appendix/%s.png'%n, format='png', dpi=300)
def getQuaternionSegmentsByRawData(highDimensionalData,quaternionData): (lowDimensionalData,explainedVariance) = pca.pca(highDimensionalData,n_components=1) (mins,maxs) = segment.segmentationPoints(lowDimensionalData[:,0]) segments = pl.split(quaternionData,maxs)[1:-1] return segments
def getHighAndLowDimSegments(highDimensionalData, n_components=3, smoothingWindow=100): (lowDimensionalData,explainedVariance) = pca.pca(highDimensionalData,n_components) (mins,maxs) = segment.segmentationPoints(lowDimensionalData[:,0], windowSize=smoothingWindow) HDsegments = pl.split(highDimensionalData,maxs)[1:-1] LDsegments = pl.split(lowDimensionalData,maxs)[1:-1] return (HDsegments,LDsegments,explainedVariance)