60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
from . import xsp, lament
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import numpy as np
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def smoothfft(xs, ys, bw=1, precision=512):
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"""performs log-lin smoothing on magnitude data,
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generally from the output of averfft."""
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lament("smoothfft(): DEPRECATED; use smoothfft2 instead.")
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xs2 = xsp(precision)
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ys2 = np.zeros(precision)
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log_xs = np.log(xs)
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for i, x in enumerate(xs2):
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dist = np.exp(np.abs(log_xs - np.log(x + 1e-35)))
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window = np.maximum(0, 1 - (dist - bw))
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# at this point we could probably
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# normalize our *triangular* window to 0-1
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# and transform it into *another* windowing function
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wsum = np.sum(window)
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ys2[i] = np.sum(ys*window/wsum)
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return xs2, ys2
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def smoothfft2(xs, ys, bw=1, precision=512, compensate=True):
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"""performs log-lin smoothing on magnitude data,
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generally from the output of averfft."""
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# this is probably implementable with FFTs now that i think about it
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xs2 = xsp(precision)
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ys2 = np.zeros(precision)
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log2_xs2 = np.log2(xs2)
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for i, x in enumerate(xs):
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# before optimizations: dist = np.abs(np.log2(xs2/(x + 1e-35)))/bw
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dist = np.abs(log2_xs2 - np.log2(x + 1e-35))/bw
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# window = np.maximum(0, 1 - dist) # triangular
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window = np.exp(-dist**2/(0.5/2)) # gaussian (untruncated)
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ys2 += ys[i]*window
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if compensate:
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_, temp = smoothfft2(xs, np.ones(len(xs)),
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bw=bw, precision=precision, compensate=False)
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ys2 /= temp
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return xs2, ys2
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def smoothfft3(ys, bw=1, precision=512):
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"""performs log-lin smoothing on magnitude data"""
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size = len(ys)
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xs = np.arange(0, 1, 1/size)
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xs2 = np.logspace(-np.log2(size), 0, precision, base=2)
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ys2 = np.zeros(precision)
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comp = np.zeros(precision)
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log2_xs2 = np.log2(xs2)
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for i, x in enumerate(xs):
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dist = np.abs(log2_xs2 - np.log2(x + 1e-35)) / bw
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window = np.exp(-dist**2 * 4) # gaussian (untruncated)
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comp += window
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ys2 += ys[i] * window
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return xs2, ys2 / comp
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