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)
stances.append(cycle[:min])
swings.append(cycle[min:])
prt.plotStas(strides, 'Strides')
prt.plotStas(stances, 'stances')
prt.plotStas(swings, 'swings')
"""
originalParts= copy.deepcopy(minedParts)
minedParts = mbp.matchFracsByPositionInCycle(minedParts)
fig = plt.figure()
#fracs.append((currTime, currAngles))
anglesSplited.append(currAngles)
currTime = [t]
currAngles = [a]
lastT = t
#fracs = ke.clusterByTime(time, angles, False, minimalCluster)
#cleanedParts, kuku = ke.cleanFracs(fracs, False)
st.plotParts(anglesSplited)
periods =[]
strides = []
stances = []
swings = []
for cluster in anglesSplited:
maximaOrder=27
clusteringGranularity=0.5
breaked = part.breakToPeriods(cluster,maximaOrder, clusteringGranularity)
for cycle in breaked:
if len(cycle)>80 and len(cycle)<180:
#periods.append(cycle)
strides.append(cycle)
min = np.argmin(cycle)
stances.append(cycle[:min])
swings.append(cycle[min:])
part.plotStas(strides, 'Strides')
part.plotStas(stances, 'stances')
part.plotStas(swings, 'swings')
plt.show()
