pyhiir
use hiir (https://github.com/unevens/hiir) library in python
HIIR is a DSP (digital signal processing) library in C++ which allows:
- Changing the sampling rate of a signal by a factor of 2
- Obtaining two signals with a pi/2 phase difference (Hilbert Transform)
Pyhiir offers wrappers for the hiir methods in python.
Usage Examples
Hilbert Transformer
from pyhiir.hiir import hiir
from pyhiir.allpass import Hilbert
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
if __name__=='__main__':
h = hiir()
# compute filter coeficients for 40db attenuation
# and transition band of 10% of the sampling frequency
c = h.compute_coefs(40, .1)
# create a hilbert transformer with allpass chains
h = Hilbert(c)
fi, fq = h.get_transfer_function()
wi, hi = signal.freqz(fi.b, fi.a, fs=2)
wq, hq = signal.freqz(fq.b, fq.a, fs=2)
# Plot the frequency response
plt.plot(wi, (np.angle(hi)), color='r')
plt.plot(wq, (np.angle(hq)), color='g')
# plt.plot(wi, (np.angle(hi) - np.angle(hq)), color='y')
plt.ylabel('Phase [rad]', color='r')
plt.xlabel('Frequency')
plt.tick_params('y', colors='r')
plt.show()
plt.plot(wi, np.abs(hi), color='r')
plt.plot(wq, np.abs(hq), color='g')
plt.xlabel('Frequency')
plt.ylabel('Magnitude [db]')
plt.ylim(0.95, 1.05)
plt.show()
Decimator Filter:
from pyhiir.hiir import hiir
from pyhiir.allpass import LowPass
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
if __name__=='__main__':
h = hiir()
# compute filter coeficients for 120db attenuation
# and transition band of 1% of the sampling frequency
c = h.compute_coefs(120, .01)
# create an half-band low pass filter with allpass chains
f = LowPass(c)
# Now get the filter transfer function for plotting
ff = f.get_transfer_function()
w, h = signal.freqz(ff.b, ff.a)
plt.grid(True)
plt.xlabel("Normalized frequency")
plt.ylabel("Amplitude [dB]")
plt.plot(w / np.pi, 20 * np.log10(abs(h)), 'b')
plt.show()
