Add 'aweight.py'

1 files changed, 48 insertions, 0 deletions

diff --git a/aweight.py b/aweight.py
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+++ b/aweight.py
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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()