from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
#%%
np.random.seed(0)
target_norm = norm.pdf(range(0,400),200,40)
source_norm = norm.pdf(range(0,400),200,30)
noise_t = target_norm.max()/5.0
noise_s = source_norm.max()/40.0
target_norm = np.random.normal(target_norm, scale=0)
source_norm = 1.5*np.random.normal(source_norm, scale=0)
#target_norm /= noise_t
#source_norm /= noise_s
winds, distance, paths = dtwpy.ddtw(target_norm, source_norm, windowtype="scband",
windowsize=100,
pattern="symmetric1")
'''
winds, distance, paths = dtwpy.dtw(target_norm, source_norm, windowtype="scband",
windowsize=100,
pattern="symmetric1",cost=True)
'''
paths = np.array(paths)
euclidean = abs(target_norm[paths[:,0]] - source_norm[paths[:,1]])
init_euclidean = abs(target_norm-source_norm)
cov = np.correlate(source_norm, target_norm,mode='same')
fig, ax = plt.subplots(3, 1, figsize=(8,4))
#eucint = np.interp(range(0,1200),paths[:,0],euclidean)
'''
query = d1
template = D2
dist = distancefunc(name="manhattan")
windowtype = "itakura"
figure_number = 1
window, distance, path = dtwpy.ddtw(query,
template,
dist=dist,
windowtype="paliwal",
windowsize=10,
pattern="symmetric1",
normalized=False,
dist_only=False)
pltdtw.plotwithQT(query, template, path, figure_number, title=" Shifted")
xaxis = [x1[0] for x1 in path]
yaxis = [y1[1] for y1 in path]
Xaxis = np.asarray(xaxis)
Yaxis = np.asarray(yaxis)
D2 = (D2) / (diff2 * 25) + 3000
d1 = d1 - 50000
x1 = np.arange(0, 3120, 50)
for ii in objects:
d2 = d21[:, ii]
query = d1
template = d2
dist = distancefunc(name="euclidean")
windowtype = "scband"
figure_number = 1
cost = dtwpy.ddtw(query,
template,
dist=dist,
windowtype=windowtype,
windowsize=1500,
pattern="symmetric1",
normalized=False,
dist_only=True)
distance = np.sqrt(cost)
dis = distance
Dis = str(dis)
Diss.append(Dis)
j = str(ii)
plt.figure(2)
plt.plot(d2, label='Column' + j)
plt.xlabel('Time')
