In [3]:
%matplotlib inline
from scipy import signal
import numpy as np
b = signal.firwin(80, 0.5, window=('kaiser', 8))
w, h = signal.freqz(b)
import matplotlib.pyplot as plt
fig = plt.figure()
plt.title('Digital filter frequency response')
ax1 = fig.add_subplot(111)
plt.semilogy(w, np.abs(h), 'b')
plt.ylabel('Amplitude (dB)', color='b')
plt.xlabel('Frequency (rad/sample)')
ax2 = ax1.twinx()
angles = np.unwrap(np.angle(h))
plt.plot(w, angles, 'g')
plt.ylabel('Angle (radians)', color='g')
plt.grid()
plt.axis('tight')
plt.show()
In [4]:
b
Out[4]:
array([ -1.33273162e-05, -2.70019765e-05, 4.66576921e-05,
7.37989478e-05, -1.10145608e-04, -1.57644341e-04,
2.18479272e-04, 2.95082069e-04, -3.90141861e-04,
-5.06615552e-04, 6.47739335e-04, 8.17042535e-04,
-1.01836525e-03, -1.25588176e-03, 1.53413234e-03,
1.85806678e-03, -2.23310434e-03, -2.66521596e-03,
3.16103712e-03, 3.72802248e-03, -4.37465793e-03,
-5.11075236e-03, 5.94784098e-03, 6.89974778e-03,
-7.98337829e-03, -9.21985328e-03, 1.06361589e-02,
1.22676005e-02, -1.41615486e-02, -1.63833327e-02,
1.90258667e-02, 2.22260702e-02, -2.61944284e-02,
-3.12718302e-02, 3.80484002e-02, 4.76407472e-02,
-6.24444580e-02, -8.86925061e-02, 1.49247170e-01,
4.49894529e-01, 4.49894529e-01, 1.49247170e-01,
-8.86925061e-02, -6.24444580e-02, 4.76407472e-02,
3.80484002e-02, -3.12718302e-02, -2.61944284e-02,
2.22260702e-02, 1.90258667e-02, -1.63833327e-02,
-1.41615486e-02, 1.22676005e-02, 1.06361589e-02,
-9.21985328e-03, -7.98337829e-03, 6.89974778e-03,
5.94784098e-03, -5.11075236e-03, -4.37465793e-03,
3.72802248e-03, 3.16103712e-03, -2.66521596e-03,
-2.23310434e-03, 1.85806678e-03, 1.53413234e-03,
-1.25588176e-03, -1.01836525e-03, 8.17042535e-04,
6.47739335e-04, -5.06615552e-04, -3.90141861e-04,
2.95082069e-04, 2.18479272e-04, -1.57644341e-04,
-1.10145608e-04, 7.37989478e-05, 4.66576921e-05,
-2.70019765e-05, -1.33273162e-05])
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