def allMotionslowDimRawSimilarity(n_components=3,savePlot=False,title="Similarity matrix"):
# Load files and get segments
(raw_v1,weights_v1)=m.getLowDimensionalSegments(m.readCSVfile("captures/VerticalArmSpin-Dan.csv"),n_components)
(raw_v2,weights_v2)=m.getLowDimensionalSegments(m.readCSVfile("captures/VerticalArmSpin-Jibran.csv"),n_components)
(raw_h1,weights_h1)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Dan.csv"),n_components)
(raw_h2,weights_h2)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components)
(raw_h3,weights_h3)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components)
# Discard bad segments
raw_h1 = raw_h1[4:]
raw_v2 = raw_v2[1:5]
# Make name labels
namesV1 = ["V1"]*len(raw_v1)
namesV2 = ["V2"]*len(raw_v2)
namesH1 = ["H1"]*len(raw_h1)
namesH2 = ["H2"]*len(raw_h2)
namesH3 = ["H3"]*len(raw_h3)
names = namesV1+namesV2+namesH1+namesH2+namesH3
# Calculate average weights
averageWeights = [(a+b+c+d+e)/5.0 for (a,b,c,d,e) in zip(weights_v1,weights_v2,weights_h1,weights_h2,weights_h3)]
# Crunch similarity matrix
m.similarityMatrix(raw_v1+raw_v2+raw_h1+raw_h2+raw_h3,names,averageWeights,title,savePlot)
def plotSegments():
m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components=1,plt=True,title="Large horizontal arm spin segments")
m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components=1,plt=True,title="Small horizontal arm spin segments")
def lowDimRawSimilarity(n_components=3):
(raw_h_large,weights_large)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpin-Jibran.csv"),n_components)
(raw_h_small,weights_small)=m.getLowDimensionalSegments(m.readCSVfile("captures/HorizontalArmSpinLittleCircles-Jibran.csv"),n_components)
names = ['Big','Big','Big','Big','Big','Big','Big','Small','Small','Small','Small','Small','Small','Small','Small','Small']
averageWeights = [(a+b)/2.0 for (a,b) in zip(weights_large,weights_small)]
m.similarityMatrix(raw_h_large+raw_h_small,names,averageWeights,"Similarity Matrix using raw sensor data projected to the latent space")
def similarityMatrixForExpandingCirclesLowDim():
print "Reading data..."
bar = progressbar.ProgressBar(maxval=12, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start(); progress = 0
data0a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius0cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data5a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius5cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data10a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius10cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data15a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius15cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data20a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius20cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data25a=m.readRaw("captures/mechanicalArm/ArmLength60cm/Anticlockwise/radius25cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data0b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius0cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data5b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius5cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data10b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius10cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data15b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius15cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data20b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius20cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
data25b=m.readRaw("captures/mechanicalArm/ArmLength60cm/Clockwise/radius25cmHorizontal.txt")[26:,4:]; progress += 1; bar.update(progress)
bar.finish()
print "Calculating segments..."
bar = progressbar.ProgressBar(maxval=12, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
bar.start(); progress = 0
(segs0a,wa0)=m.getLowDimensionalSegments(data0a,n_components=2); progress += 1; bar.update(progress)
(segs5a,wa5)=m.getLowDimensionalSegments(data5a,n_components=2); progress += 1; bar.update(progress)
(segs10a,wa10)=m.getLowDimensionalSegments(data10a,n_components=2); progress += 1; bar.update(progress)
(segs15a,wa15)=m.getLowDimensionalSegments(data15a,n_components=2); progress += 1; bar.update(progress)
(segs20a,wa20)=m.getLowDimensionalSegments(data20a,n_components=2); progress += 1; bar.update(progress)
(segs25a,wa25)=m.getLowDimensionalSegments(data25a,n_components=2); progress += 1; bar.update(progress)
(segs0b,wb0)=m.getLowDimensionalSegments(data0b,n_components=2); progress += 1; bar.update(progress)
(segs5b,wb5)=m.getLowDimensionalSegments(data5b,n_components=2); progress += 1; bar.update(progress)
(segs10b,wb10)=m.getLowDimensionalSegments(data10b,n_components=2); progress += 1; bar.update(progress)
(segs15b,wb15)=m.getLowDimensionalSegments(data15b,n_components=2); progress += 1; bar.update(progress)
(segs20b,wb20)=m.getLowDimensionalSegments(data20b,n_components=2); progress += 1; bar.update(progress)
(segs25b,wb25)=m.getLowDimensionalSegments(data25b,n_components=2); progress += 1; bar.update(progress)
segs20a=segs20a[:-1]
segs25a=segs25a[1:]
segs0b=segs0b[1:-1]
segs5b=segs5b[1:]
segs10b=segs10b[:-1]
segs15b=segs15b[1:-1]
segs20b=segs20b[1:-1]
segs25b=segs25b[1:-1]
names = ["0a"]*len(segs0a)+["5a"]*len(segs5a)+["10a"]*len(segs10a)+["15a"]*len(segs15a)+["20a"]*len(segs20a)+["25a"]*len(segs25a)+["0c"]*len(segs0b)+["5c"]*len(segs5b)+["10c"]*len(segs10b)+["15c"]*len(segs15b)+["20c"]*len(segs20b)+["25c"]*len(segs25b)
weights = [float(sum(t))/float(len(t)) for t in zip(wa0,wa5,wa10,wa15,wa20,wa25,wb0,wb5,wb10,wb15,wb20,wb25)]
bar.finish()
print "Calculating similarities..."
m.similarityMatrix(segs0a+segs5a+segs10a+segs15a+segs20a+segs25a+segs0b+segs5b+segs10b+segs15b+segs20b+segs25b,names,weights,"Similarity Matrix using two principal components",savePlot=True)
