import numpy as np
import matplotlib.pyplot as plt
from tslearn.clustering import KShape
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
x = np.arange(0, 10, 0.1)
sincos=np.zeros((20, 100), dtype=float)

for i in range (0,10):
 sincos[i] = np.sin(x)+np.random.normal(scale=0.1, size=len(x))
 sincos[i+10] = np.cos(x)+np.random.normal(scale=0.1, size=len(x))
 sincos[i,0]=sincos[i,0]+4
 sincos[10+i,0]=sincos[10+i,0]+4
sincos = TimeSeriesScalerMeanVariance().fit_transform(sincos)
ks = KShape(n_clusters=2,verbose=True)
y_pred = ks.fit_predict(sincos)
plt.figure(figsize=(13,5))
for i in range(2):
    plt.subplot(2, 1, 1 + i)
    for xx in sincos[y_pred == i]:
        plt.plot(xx[0:100].ravel(), "k-" ,alpha=.2)
    plt.plot(ks.cluster_centers_[i,0:100].ravel(), "r-")
    plt.xlim(0, 100)
    plt.ylim(-2, 4)
    plt.title(f"Cluster {str(i + 1)} has {str(sincos[y_pred == i].shape[0])} timeseries")
plt.tight_layout()
plt.show()