gain = 29
f0 = 440*2**(1/12)
hwidth = 10
step = 1
f = np.arange(f0-hwidth, f0+hwidth+step, step)
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method|f_norm|a_norm, d, gain)
buflen = det.getBuflen()
channels = det.getChans()
z = np.zeros((len(f),len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.seek(0)
det.getZ(z)
m = det.absZ(r, z)
######## matching straight line and decaying exponential
## get portion of response that corresponds to single note
#t = np.linspace(0, len(audio), len(audio))
t0 = 6*sr
t1 = int(9*sr)
bandwidth = np.zeros(len(f))
det_char = np.array(list(zip(f, bandwidth)))
det = DetectorBank(sr, audio.astype(np.float32), 4, det_char,
method | f_norm | a_norm, d, gain)
p = Producer(det)
seg_len = sr // 2
cache = DetectorCache(p, 2, seg_len)
channels = det.getChans()
N = 1000
prep = Preprocessor(cache, seg_len, np.arange(channels, dtype=np.int_), N)
od = OnsetDetector(prep, np.arange(channels, dtype=np.int_), sr)
gradient = np.array([od.do_stuff(n) for n in range(det.getBuflen())])
onsets = od.onsets
offsets = od.offsets
# reset DetectorBank to beginning and get values (for plotting)
z = np.zeros((len(f), len(audio)), dtype=np.complex128)
r = np.zeros(z.shape)
det.seek(0)
det.getZ(z)
det.absZ(r, z)
t = np.linspace(0, len(audio) / sr, len(audio))
c = [
'black', 'blue', 'chocolate', 'cyan', 'darkmagenta', 'darkorange',