49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
# Run with "ipython -i --matplotlib=qt correlate.py"
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#
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from __future__ import print_function
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import numpy as np
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# import pandas as pd
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from scipy import signal
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import matplotlib.pyplot as plt
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RATE = 44100
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# tone synthesis
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def tone(freq, cycles, amp=1, rate=RATE):
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len = cycles * (1.0/freq)
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t = np.linspace(0, len, len * rate)
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if freq is 0:
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data = np.zeros(int(len * rate))
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else:
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data = np.sin(2 * np.pi * freq * t) * amp
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return data
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freqs = range(600, 722, 2) # Frequency range
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fig, ax = plt.subplots(1, 1, sharex=True)
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for length in [32, 64, 100, 128, 256]:
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print("len = ", length)
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sig = tone(660.0, length) # Reference tone
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response = []
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for freq in freqs:
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sig_tx = tone(freq * 1.0, length) # Test tone
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resp = np.abs(signal.correlate(sig, sig_tx, mode='same'))
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response.append(resp.sum())
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ax.semilogy(freqs, response, label='len = {}'.format(length))
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ax.set_title('Matched filter response')
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ax.axvline(626.67, ls=':') # Guardband markers
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ax.axvline(695.01, ls=':')
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ax.legend(loc='best')
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ax.margins(0, 0.1)
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fig.set_tight_layout(True)
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fig.show()
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