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')