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StepExtraction.py
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StepExtraction.py
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from scipy.signal import argrelmin,argrelmax
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import FeatureKonstruktion
import FourierTransformation
import Init
import pandas as pd
import math
import pylab as plb
__author__ = 'Sebastian'
def getminimas(dataMatrix, Sensor=[290]):
signal = dataMatrix[:,Sensor[0]]
maxAbsValue, maxAbsFreq = FourierTransformation.maxAbsFreq(signal[0:13000])
Filtered = FeatureKonstruktion.filter(dataMatrix,Sensor,maxAbsFreq)
print maxAbsFreq,maxAbsValue
plt.plot(signal)
plt.show()
return argrelmin(Filtered[:,Sensor],order=25)
def getmaximas(dataMatrix, Sensor=[290]):
signal = dataMatrix[:,Sensor[0]]
maxAbsValue, maxAbsFreq = FourierTransformation.maxAbsFreq(signal[0:13000])
Filtered = FeatureKonstruktion.filter(dataMatrix,Sensor,maxAbsFreq)
plt.plot(Filtered[:,Sensor],label="z-Acceleration Foot")
plt.title("Filtered acceleration")
plt.legend()
plt.xlabel("Samples")
plt.ylabel("m/s^2")
plt.show()
return argrelmax(Filtered[:,Sensor],order=25)
def stepDetectionbackmiddle(dataMatrix):
minimas = getminimas(dataMatrix)
maxima = minimas[0]
newmatrix = dataMatrix
for i in range(0,maxima[0]):
newmatrix[i,0]= maxima[0]
for j in range(0,len(maxima)-1):
middle = ((maxima[j]+maxima[j+1])/2)
for k in range(maxima[j],middle):
newmatrix[k,0]= maxima[j]
for l in range(middle,maxima[j+1]):
newmatrix[l,0] = maxima[j+1]
for z in range(maxima[len(maxima)-1],len(dataMatrix[:,0])):
newmatrix[z,0] = maxima[len(maxima)-1]
return np.c_[dataMatrix, newmatrix[:,0]]
def stepDetectionback(dataMatrix):
minimas = getmaximas(dataMatrix)
maxima = minimas[0]
newmatrix = dataMatrix
Steps = []
for i in range(0,maxima[0]):
newmatrix[i,0]= maxima[0]
Steps.append([0,maxima[0]])
for j in range(0,len(maxima)-1):
for k in range(maxima[j],maxima[j+1]):
newmatrix[k,0]= maxima[j]
Steps.append([maxima[j],maxima[j+1]])
for z in range(maxima[len(maxima)-1],len(dataMatrix[:,0])):
newmatrix[z,0] = maxima[len(maxima)-1]
Steps.append([maxima[len(maxima)-1],len(dataMatrix[:,0])])
Steparray = np.array(Steps)
return Steparray, np.c_[dataMatrix, newmatrix[:,0]]
def stepDetectionTwoFoot(dataMatrix):
left = getmaximas(dataMatrix,Sensor=[224])
print left[0]
right = getmaximas(dataMatrix,Sensor=[290])
print right[0]
maxima= np.concatenate((left[0],right[0]),axis=0)
maxima = sorted(maxima)
newmatrix = dataMatrix
for i in range(0,maxima[0]):
newmatrix[i,0]= maxima[0]
for j in range(0,len(maxima)-1):
middle = ((maxima[j]+maxima[j+1])/2)
for k in range(maxima[j],middle):
newmatrix[k,0]= maxima[j]
for l in range(middle,maxima[j+1]):
newmatrix[l,0] = maxima[j+1]
for z in range(maxima[len(maxima)-1],len(dataMatrix[:,0])):
newmatrix[z,0] = maxima[len(maxima)-1]
return np.c_[dataMatrix, newmatrix[:,0]]
def stepDetectionleft(dataMatrix):
minimas = getmaximas(dataMatrix,Sensor=[202])
maxima = minimas[0]
newmatrix = dataMatrix
Steps = []
for i in range(0,maxima[0]):
newmatrix[i,0]= maxima[0]
Steps.append([0,maxima[0]])
for j in range(0,len(maxima)-1):
for k in range(maxima[j],maxima[j+1]):
newmatrix[k,0]= maxima[j]
Steps.append([maxima[j],maxima[j+1]])
for z in range(maxima[len(maxima)-1],len(dataMatrix[:,0])):
newmatrix[z,0] = maxima[len(maxima)-1]
Steps.append([maxima[len(maxima)-1],len(dataMatrix[:,0])])
extractedstep = np.array(Steps)
return extractedstep, np.c_[dataMatrix, newmatrix[:,0]]
def stepDetectionright(dataMatrix):
minimas = getmaximas(dataMatrix,Sensor=[268])
maxima = minimas[0]
newmatrix = dataMatrix
Steps = []
for i in range(0,maxima[0]):
newmatrix[i,0]= maxima[0]
Steps.append([0,maxima[0]])
for j in range(0,len(maxima)-1):
for k in range(maxima[j],maxima[j+1]):
newmatrix[k,0]= maxima[j]
Steps.append([maxima[j],maxima[j+1]])
for z in range(maxima[len(maxima)-1],len(dataMatrix[:,0])):
newmatrix[z,0] = maxima[len(maxima)-1]
Steps.append([maxima[len(maxima)-1],len(dataMatrix[:,0])])
extractedstep = np.array(Steps)
return extractedstep, np.c_[dataMatrix, newmatrix[:,0]]
def videosteps(dataMatrix, elan):
newMatrix= dataMatrix
newlan =elan
elan = pd.DataFrame(elan[:,1:],index=elan[:,0])
elan = np.array(elan.ix['1Passgang'])
print len(elan)
print elan
elan[:,:2] = elan[:,:2]/10
elanhalbe = elan[:,:2]/2
stepright, noneed = stepDetectionright(newMatrix)
stepleft, noneed = stepDetectionleft(newMatrix)
steps = np.concatenate((stepright,stepleft))
realsteps = []
for i in range(0,len(elan)):
nearest = find_nearest(steps[:,0],elanhalbe[i,0])
nearest2 = steps[nearest,0]
nearest = steps[nearest,1]
realsteps.append([int(nearest2),int(nearest),int(elan[i,0])*20,int(elan[i,1])*20])
realsteps = np.array(realsteps)
plt.plot(newMatrix[:,202],label = "z-Vektor")
plt.axvline(3,color ='r',label="hillclimber step")
plt.axvline(3,color ='g',label="videolabeled step")
plt.legend()
plt.title("Stepextraction")
plt.xlabel("Samples")
for xs in elanhalbe[:,0]:
plt.axvline(x=xs,color = 'r')
for xs in elanhalbe[:,1]:
plt.axvline(x=xs,color = 'r')
for xs in realsteps[:,0]:
plt.axvline(x=xs,color = 'g')
for xs in realsteps[:,1]:
plt.axvline(x=xs,color = 'g')
#for xs in stepleft[:,0]:
# plt.axvline(x=xs,color = 'g')
#for xs in stepleft[:,1]:
# plt.axvline(x=xs,color = 'g')
plt.show()
return newMatrix, realsteps
def findsync(dataMatrix):
datamatrix = Init.getData(dataMatrix,sensors=["STE"],datas=[ "acc"])
signal= dataMatrix[:,2]
maxAbsValue, maxAbsFreq = FourierTransformation.maxAbsFreq(signal)
Filtered = FeatureKonstruktion.filter(datamatrix,[2,3,4],maxAbsFreq)
plt.plot(Filtered[:,2:])
plt.show()
def find_nearest(array,value):
idx = (np.abs(array-value)).argmin()
return idx