def histogramStride(dataMatrix):
matrixnew = dataMatrix
Steparray, step = StepExtraction.stepDetectionback(matrixnew)
step1 = pd.DataFrame(step)
step1 = step1.iloc[:,310]
step1= np.array(step1.value_counts())
plt.hist(step1)
plt.show()
def MatrixforAndroid(datamatrix, elan):
modifiedmatrix ,steps = StepExtraction.videosteps(datamatrix,elan)
print steps
print elan
e,f = FeatureKonstruktion.handverkrampft(modifiedmatrix,steps[:,[0,1]])
g,h,k = FeatureKonstruktion.aufrechtgehen(modifiedmatrix,steps[:,[0,1]])
i,j =FeatureKonstruktion.Fussabrollen(modifiedmatrix,steps[:,[0,1]])
passgang = FeatureKonstruktion.Passgang(modifiedmatrix,steps[:,[0,1]])
rightpeak,leftpeak = FeatureKonstruktion.Stockaufsatz(modifiedmatrix,steps[:,[0,1]])
a,b = FeatureKonstruktion.schulterbewegung(modifiedmatrix,steps[:,[0,1]])
c,d = FeatureKonstruktion.armstreckung(modifiedmatrix,steps[:,[0,1]])
elan = pd.DataFrame(elan[:,1:],index=elan[:,0])
elanpass = np.array(elan.ix['1Passgang'])[:,3]
elanstock = np.array(elan.ix['1Stockeinsatz'])[:,3]
elanarm = np.array(elan.ix['1Armeinsatz'])[:,3]
elanschritt = np.array(elan.ix['2Schrittlaenge'])[:,3]
elanschub = np.array(elan.ix['2Schub'])[:,3]
elanverkrampfung = np.array(elan.ix['2Verkrampfung'])[:,3]
elanober = np.array(elan.ix['2Oberkoerper'])[:,3]
elanfuss = np.array(elan.ix['2Fussaufsatz'])[:,3]
elantiming = np.array(elan.ix['2Timing'])[:,3]
elanshwingen = np.array(elan.ix['3Vorschwingen'])[:,3]
elanblick = np.array(elan.ix['3Blick'])[:,3]
print len(steps)
print len(elanpass)
print len(elanstock)
print len(elanarm)
print len(elanschritt)
print len(elanschub)
print len(elanfuss)
print len(elanverkrampfung)
print len(elanober)
print len(elantiming)
print len(elanshwingen)
print len(elanblick)
new = np.c_[steps,elanpass]
new = np.c_[new,elanstock]
new = np.c_[new,elanarm]
new = np.c_[new,elanschritt]
new = np.c_[new,elanschub]
new = np.c_[new,elanfuss]
new = np.c_[new,elanverkrampfung]
new = np.c_[new,elanober]
new = np.c_[new,elantiming]
new = np.c_[new,elanshwingen]
new = np.c_[new,elanblick]
plt.subplot(4,1,1)
plt.title("passgang")
plt.plot(passgang)
plt.subplot(4,1,2)
plt.title("Stockaufsatz")
plt.plot(rightpeak)
plt.plot(leftpeak)
plt.subplot(4,1,3)
plt.title("schulter")
plt.plot(a)
plt.plot(b, label= "Rechteschulter")
plt.subplot(4,1,4)
plt.title("Arm")
plt.plot(c)
plt.plot(d)
#plt.plot(d, label= "Rechter Arm")
plt.show()
file = []
label= []
FeatureFile= np.c_[steps,passgang,rightpeak,leftpeak,a,b,c,d,e,f,g,h,i,j,k]
for k in range(4,len(new[1,:])):
for t in range(0,len(new)):
if new[t,k]==3 or new[t,k]==2 or new[t,k]==1:
temp = new[t,k]
else:
new[t,k]= temp
for i in range(0,len(new)):
temp = new[i,4]
if temp==3:
new[i,4:]=3
for i in range(0,len(new)):
if new[i,4]==2 or new[i,4]==1:
print new[i,4]
file.append(FeatureFile[i,:])
label.append(new[i,:])
labelpass = []
filepass = []
for i in range(0,len(new)):
if new[i,4]==2 or new[i,4]==1 or new[i,4]== 3:
filepass.append(FeatureFile[i,:])
labelpass.append(new[i,:5])
print(label)
print(file)
np.savetxt("20151127ID005features.csv",file,delimiter="\t")
np.savetxt("20151127ID005labels.csv",label,delimiter="\t",fmt="%s")
np.savetxt("20151127ID005featurespass.csv",filepass,delimiter="\t")
np.savetxt("20151127ID005labelspass.csv",labelpass,delimiter="\t",fmt="%s")
print "finished"
def wholedataelan(dataMatrix,elan):
matrix, step = StepExtraction.videosteps(dataMatrix[:,:],elan)
print step
elan = pd.DataFrame(elan[:,1:],index=elan[:,0])
elanpass = np.array(elan.ix['1Passgang'])[:,3]
elanstock = np.array(elan.ix['1Stockeinsatz'])[:,3]
elanarm = np.array(elan.ix['1Armeinsatz'])[:,3]
elanschritt = np.array(elan.ix['2Schrittlaenge'])[:,3]
elanschub = np.array(elan.ix['2Schub'])[:,3]
elanverkrampfung = np.array(elan.ix['2Verkrampfung'])[:,3]
elanober = np.array(elan.ix['2Oberkoerper'])[:,3]
elanfuss = np.array(elan.ix['2Fussaufsatz'])[:,3]
elantiming = np.array(elan.ix['2Timing'])[:,3]
elanshwingen = np.array(elan.ix['3Vorschwingen'])[:,3]
elanblick = np.array(elan.ix['3Blick'])[:,3]
print len(step)
print len(elanpass)
print len(elanstock)
print len(elanarm)
print len(elanschritt)
print len(elanschub)
print len(elanfuss)
print len(elanverkrampfung)
print len(elanober)
print len(elantiming)
print len(elanshwingen)
print len(elanblick)
new = np.c_[step,elanpass]
new = np.c_[new,elanstock]
new = np.c_[new,elanarm]
new = np.c_[new,elanschritt]
new = np.c_[new,elanschub]
new = np.c_[new,elanfuss]
new = np.c_[new,elanverkrampfung]
new = np.c_[new,elanober]
new = np.c_[new,elantiming]
new = np.c_[new,elanshwingen]
new = np.c_[new,elanblick]
print new[:,4]
for k in range(4,len(new[1,:])):
for t in range(0,len(new)):
if new[t,k]==3 or new[t,k]==2 or new[t,k]==1:
temp = new[t,k]
else:
new[t,k]= temp
for i in range(0,len(new)):
temp = new[i,4]
if temp==3:
print "test"
new[i,4:]=3
labeldata = []
for i in range(0,len(new)):
if new[i,4]==3 or new[i,4]==2 or new[i,4]==1:
print new[i,4]
labeldata.append(new[i,:])
labeldata = np.array(labeldata)
print labeldata
matrixsteps = pd.DataFrame(matrix[labeldata[0,0]:labeldata[0,1],:])
matrix = pd.DataFrame(matrix)
for i in range(1,len(labeldata)):
matrixsteps = pd.concat([matrixsteps,matrix.iloc[labeldata[i,0]:labeldata[i,1],:]])
label = []
for i in range(0,len(labeldata)):
for k in range(labeldata[i,0],labeldata[i,1]):
labels = np.array(labeldata[i,4:])
#labels = map(int,labels)
label.append(labels)
matrixstepspass = np.array(matrixsteps)
labelpass = np.array(label)
print matrixstepspass
print labelpass[:,4]
print(len(matrixstepspass))
print(len(labelpass))
plt.subplot(2,1,1)
plt.plot(matrixstepspass[:,2:5])
plt.subplot(2,1,2)
plt.plot(labelpass[:,0])
plt.show()
np.savetxt("dataMatrixpass.csv",matrixstepspass,delimiter="\t")
np.savetxt("Labelspass.csv",labelpass[:,0],delimiter="\t",fmt="%s")
label =[]
for i in range(0,len(labeldata)):
temp = labeldata[i,4]
if temp != 3:
label.append(labeldata[i,:])
label = np.array(label)
print label
matrix=np.array(matrix)
matrixsteps = pd.DataFrame(matrix[label[0,0]:label[0,1],:])
matrix = pd.DataFrame(matrix)
for i in range(1,len(label)):
matrixsteps = pd.concat([matrixsteps,matrix.iloc[label[i,0]:label[i,1],:]])
label = label[:,:]
labelnew = []
for i in range(0,len(label)):
for k in range(label[i,0],label[i,1]):
labels = np.array(label[i,4:])
#labels = map(int,labels)
labelnew.append(labels)
print(matrixsteps)
print(labelnew)
print len(matrixsteps)
print len(labelnew)
plt.subplot(2,1,1)
plt.plot(matrixsteps.iloc[:,2:5])
plt.subplot(2,1,2)
plt.plot(labelnew)
plt.show()
np.savetxt("dataMatrix.csv",matrixsteps,delimiter="\t")
np.savetxt("Labels.csv",labelnew,delimiter="\t",fmt="%s")