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from scipy import signal
from waveform_analysis import A_weighting
from math import pi
import numpy
import matplotlib.pyplot as plt
# Create our X axis values, the frequencies to test at:
gs = 2 * pi * numpy.geomspace(10, 24000, 1000)
# This is the ideal A-weighting filter:
z, p, k = A_weighting(48000, output='zpk')
_, hA = signal.freqz_zpk(z, p, k, gs, fs=48000)
# And here is our three-stage implementation:
z, p, k = signal.iirfilter(1, [640, 10000], btype='bandpass', analog=False, fs=48000, output='zpk')
_, hB = signal.freqz_zpk(z, p, k * 1.17, gs, fs=48000)
z, p, k = signal.iirfilter(1, 12000, btype='lowpass', analog=False, fs=48000, output='zpk')
_, hL = signal.freqz_zpk(z, p, k, gs, fs=48000)
z, p, k = signal.iirfilter(1, 120, btype='highpass', analog=False, fs=48000, output='zpk')
_, hH = signal.freqz_zpk(z, p, k, gs, fs=48000)
# Lastly, our SOS approximation:
sos = [[ 0.5, 0, -0.5, 1, -1.062, 0.14],
[ 0.5, 0.5, 0, 1, 0, 0 ],
[ 1.0, -1.0, 0, 1, -0.985, 0 ]]
wS, hS = signal.sosfreqz(sos, fs=48000)
# Plot everything:
plt.semilogx(gs, 20 * numpy.log10(hA), label='A')
plt.semilogx(gs, 20 * numpy.log10(hB * hL * hH), label='bp+lp+hp')
plt.semilogx(wS, 20 * numpy.log10(hS), label='sos')
plt.title('Frequency response')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [dB]')
plt.ylim(-30, 10)
plt.xlim(20, 24000)
plt.grid(True, color='0.7', linestyle='-', which='major', axis='both')
plt.grid(True, color='0.9', linestyle='-', which='minor', axis='both')
plt.legend()
plt.show()
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