dist = distancefunc(name="manhattan")
windowtype="itakura"
figure_number = 1
window, distance, path = dtwpy.dtw(query, template, dist=dist, windowtype=windowtype, windowsize=50,pattern="symmetric1", normalized=False,dist_only=False, cost=True)
T = ii - P
CC = C[T]
L = str(ii)
pltt.plotting(query, template, e, f, path,figure_number,L,CC)
print('Column '+L+' plotted')
a = np.arange(0,3120,1)
b = np.arange(0,3120,1)
c = []
for i in a:
c.append((a[i],b[i]))
dist=dist,
windowtype=windowtype,
windowsize=1500,
pattern="symmetric1",
normalized=False,
dist_only=False,
cost=True)
CC = C[ii]
L = str(ii)
dis = distance
Dis = str(dis)
pltt.plotting(query, template, e, f, path, figure_number, L,
CC) #Uncomment this if you want to see the alignment plotted
Diss.append(Dis)
plt.figure(3)
plt.plot(x, y1, 'b--', label='Original amplitude normal distribution')
plt.plot(x, y2, 'r--', label='Higher amplitude normal distribution')
plt.plot(
x3,
y3,
'g--',
label='Original amplitude normal distribution with shifted mean(+200)')
#plt.plot(x4,y4,'c--',label='Original amplitude normal distribution with shifted mean(-400)')
#plt.plot(x5,y5,'m--',label='Original amplitude normal distribution with shifted mean(-100)')
#plt.plot(x,y6,'y--',label='Lower amplitude normal distribution')
plt.plot(
