def cleanFracs(fracs, plot=False, MAwindowSize=8, MAexp=1.4):
frameSize = math.ceil(np.sqrt(len(fracs)))
cleanedParts = []
originalParts = []
if(plot):
pass
#figOrigin = plt.figure()
#figCleaned = plt.figure()
i=1
for time, values in fracs:
if(plot):
pass
#curr = figOrigin.add_subplot(frameSize, frameSize, i)
#curr.plot(time,values)
#length = int((time[-1] - time[0]) / 30)
time, values = inter.getUniformSampled(time, values)
originalParts.append(values)
cleanesdValues = ma.movingAverage(values, MAwindowSize, MAexp)
if(plot):
plt.figure()
plt.plot(values)
plt.plot(cleanesdValues)
plt.show()
#curr = figCleaned.add_subplot(frameSize, frameSize, i)
#curr.plot(time,values)
cleanedParts.append(cleanesdValues)
i+=1
return cleanedParts, originalParts
def analyzeDataInner(time, data, label):
vec = []
featuresNames = []
v, f = getStats(data, label)
vec += v
featuresNames += f
_, un = inter.getUniformSampled(time, data)
cleaned = ma.movingAverage(un, 20, 1.1)
v, f = getStats(cleaned, label + "after LPF ")
vec += v
featuresNames += f
velocity = np.diff(cleaned)
v, f = getStats(velocity, label + " velocity")
vec += v
featuresNames += f
acceleration = np.diff(velocity)
v, f = getStats(acceleration, label + " acceleration")
vec += v
featuresNames += f
jurk = np.diff(acceleration)
v, f = getStats(jurk, label + " jurk")
vec += v
featuresNames += f
return vec, featuresNames
def plotResults(self, vec, positive, negetive):
plt.figure()
#plt.plot(vec)
filtered = ma.movingAverage(vec, 50, 1.0)
#plt.plot(filtered)
filtered = pu.normalizeVector(filtered)
plt.plot(filtered)
change = [filtered[i+1]-filtered[i] for i in range(len(filtered)-1)]
change = ma.movingAverage(change, 75, 1.0)
change = pu.normalizeVector(change)
plt.plot(change)
#plt.figure()
clustersByPercentilies = spike.clusterByPercemtile(change, 700, 80)
plt.plot(clustersByPercentilies)
clustersByPercentilies = pu.smoothOutliers(clustersByPercentilies)
plt.plot(clustersByPercentilies)
#plt.figure()
ranges = self.prepareRanges(clustersByPercentilies)
self.printConfedence(filtered, ranges, positive, negetive)
plt.show()
row=[]
for p2 in minedParts:
dis = getDistanceBetweenFracs(p1, p2, lenOfCycle)
row.append(dis)
mat.append(row)
print mat
path = solve_tsp( mat )
print path
#whole = qu.orderWithCost(minedParts, getDistanceBetweenFracs, appendFracs)
whole = minedParts[path[0]]
for i in path[1:]:
whole = appendFracs(whole, minedParts[path[i]], lenOfCycle)
whole = whole[0]
plt.figure()
whole = ma.movingAverage(whole, 10, 1.3)
plt.plot(whole)
#plt.plot()
#plt.show()
periods =[]
strides = []
stances = []
swings = []
maximaOrder=15
clusteringGranularity=0.5
breaked = prt.breakToPeriods(whole,maximaOrder, clusteringGranularity)
for cycle in breaked:
if len(cycle)>25 and len(cycle)<60:
#periods.append(cycle)
strides.append(cycle)
min = np.argmin(cycle)
def LPF(self):
self.analysisVec = ma.movingAverage(self.analysisVec, 30, 1.1)
return self.analysisVec
