49 lines
1.1 KiB
Python
49 lines
1.1 KiB
Python
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from . import rfft
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import numpy as np
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import scipy.signal as sig
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def magnitudes_window_setup(s, size=8192):
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L = s.shape[0]
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overlap = 0.661
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step = np.ceil(size*(1 - overlap))
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segs = np.ceil(L/step)
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return step, segs
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def magnitudes(s, size=8192):
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import scipy.linalg as linalg
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step, segs = magnitudes_window_setup(s, size)
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L = s.shape[0]
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# blindly pad with zeros for friendlier ffts and overlapping
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z = np.zeros(size)
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s = np.hstack((s, z))
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win_size = size
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win = sig.blackmanharris(win_size)
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win /= linalg.norm(win)
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count = 0
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for i in range(0, L - 1, int(step)):
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windowed = s[i:i+win_size]*win
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power = np.abs(rfft(windowed, size))**2
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# this scraps the nyquist value to get exactly size outputs
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yield power[0:size]
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count += 1
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#assert(segs == count)
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def averfft(s, size=8192):
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"""calculates frequency magnitudes by fft and averages them together."""
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step, segs = magnitudes_window_setup(s, size)
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avg = np.zeros(size)
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for power in magnitudes(s, size):
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avg += power/segs
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avg_db = 10*np.log10(avg)
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return avg_db
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