import numpy as np
import matplotlib.pylab as plt
import padasip as pa
import matplotlib.pyplot as plt
from scipy.io import wavfile

N = 500
x = np.random.normal(0, 1, (N, 4)) # input matrix
v = np.random.normal(0, 0.1, N) # noise
d = 2*x[:,0] + 0.1*x[:,1] - 4*x[:,2] + 0.5*x[:,3] + v # target



fs1, data1 = wavfile.read('szumwsamraz.wav')
fs2, data2 = wavfile.read('result.wav')
fs3, data3 = wavfile.read('szum.wav')

d = data2[:,1]
x = data1[:,1]
print (len(d))
print (len(x))

# identification
f = pa.filters.AdaptiveFilter(model="NLMS", n=4, mu=0.1, w="random")
y, e, w = f.run(d, x)

## show results
plt.figure(figsize=(15,9))
plt.subplot(211);plt.title("Adaptation");plt.xlabel("samples - k")
plt.plot(d,"b", label="d - target")
plt.plot(y,"g", label="y - output");plt.legend()
plt.subplot(212);plt.title("Filter error");plt.xlabel("samples - k")
plt.plot(10*np.log10(e**2),"r", label="e - error [dB]");plt.legend()
plt.tight_layout()
plt.show()