60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
from . import xsp
|
|
import numpy as np
|
|
|
|
def smoothfft(xs, ys, bw=1, precision=512):
|
|
"""performs log-lin smoothing on magnitude data,
|
|
generally from the output of averfft."""
|
|
# TODO: option to extrapolate (pad) fft data
|
|
xs2 = xsp(precision)
|
|
ys2 = np.zeros(precision)
|
|
log_xs = np.log(xs)
|
|
for i, x in enumerate(xs2):
|
|
dist = np.exp(np.abs(log_xs - np.log(x + 1e-35)))
|
|
window = np.maximum(0, 1 - (dist - bw))
|
|
# at this point you could probably
|
|
# normalize our *triangular* window to 0-1
|
|
# and transform it into *another* windowing function
|
|
wsum = np.sum(window)
|
|
ys2[i] = np.sum(ys*window/wsum)
|
|
return xs2, ys2
|
|
|
|
def smoothfft2(xs, ys, bw=1, precision=512, compensate=True):
|
|
"""performs log-lin smoothing on magnitude data,
|
|
generally from the output of averfft."""
|
|
xs2 = xsp(precision)
|
|
ys2 = np.zeros(precision)
|
|
log2_xs2 = np.log2(xs2)
|
|
for i, x in enumerate(xs):
|
|
#dist = np.abs(np.log2(xs2/(x + 1e-35)))/bw
|
|
dist = np.abs(log2_xs2 - np.log2(x + 1e-35))/bw
|
|
#window = np.maximum(0, 1 - dist) # triangle window
|
|
window = np.exp(-dist**2/(0.5/2)) # gaussian function (non-truncated)
|
|
ys2 += ys[i]*window
|
|
if compensate:
|
|
_, temp = smoothfft2(xs, np.ones(len(xs)), bw=bw, precision=precision, compensate=False)
|
|
ys2 /= temp
|
|
return xs2, ys2
|
|
|
|
def smoothfft3(xs, ys, bw=1, precision=1024):
|
|
# actually this will never work...
|
|
# you need to go back to smoothfft2,
|
|
# which technically works as-designed,
|
|
# and fix the compensation to work with widely-spaced data.
|
|
raise Exception("smoothfft3 is broken.")
|
|
xs2 = xsp(precision)
|
|
ys2 = np.zeros(precision)
|
|
step = (xs[1] - xs[0])
|
|
if True:
|
|
for i, x in enumerate(xs):
|
|
dist = np.abs(xs2 - x)
|
|
bw2 = x*bw/2
|
|
window = np.maximum(0, 1 - dist/bw2)
|
|
#window = np.minimum(1, np.maximum(0, 1 - (dist - bw)))
|
|
ys2 += ys[i]*window
|
|
else:
|
|
for i, x2 in enumerate(xs2):
|
|
dist = np.abs(xs - x2)
|
|
window = np.maximum(0, 1 - (dist/step/bw))
|
|
wsum = np.sum(window)
|
|
ys2[i] = np.sum(ys*window/wsum)
|
|
return xs2, ys2
|