60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import numpy as np
|
|
import scipy.signal as sig
|
|
|
|
from .util import lament
|
|
|
|
|
|
def magnitudes_window_setup(s, size=8192, overlap=0.661):
|
|
# note: the default overlap value is only
|
|
# optimal for a blackman-harris window.
|
|
L = s.shape[0]
|
|
step = np.ceil(size*(1 - overlap))
|
|
segs = np.ceil(L/step)
|
|
return step, segs
|
|
|
|
|
|
def magnitudes(s, size=8192, broken=True):
|
|
if broken:
|
|
lament("magnitudes(broken=True): DEPRECATED")
|
|
|
|
step, segs = magnitudes_window_setup(s, size)
|
|
|
|
L = s.shape[0]
|
|
|
|
# blindly pad with zeros for friendlier ffts and overlapping
|
|
z = np.zeros(size)
|
|
s = np.r_[s, z]
|
|
|
|
win_size = size
|
|
|
|
win = sig.blackmanharris(win_size)
|
|
win /= np.sqrt(np.sum(np.square(win)))
|
|
|
|
for i in range(0, L - 1, int(step)):
|
|
windowed = s[i:i+win_size]*win
|
|
if broken:
|
|
power = np.abs(np.fft.rfft(windowed, 2 * size))**2
|
|
# this scraps the nyquist value to get exactly 'size' outputs
|
|
yield power[:size]
|
|
else:
|
|
power = np.abs(np.fft.rfft(windowed, size))**2
|
|
# this scraps the 0 Hz value to get exactly size//2 outputs
|
|
yield power[1:]
|
|
|
|
|
|
def averfft(s, size=8192, broken=True):
|
|
"""calculates frequency magnitudes by fft and averages them together."""
|
|
step, segs = magnitudes_window_setup(s, size)
|
|
|
|
if broken:
|
|
lament("averfft(broken=True): DEPRECATED")
|
|
avg = np.zeros(size)
|
|
for power in magnitudes(s, size):
|
|
avg += power/segs
|
|
else:
|
|
avg = np.zeros(size//2)
|
|
for power in magnitudes(s, size):
|
|
avg += power/segs
|
|
|
|
avg_db = 10*np.log10(avg)
|
|
return avg_db
|