R_MIN, R_MAX = 0, 5
K_MIN, K_MAX = -5, 5
# Making sure that there's W_MIN_DIFF between all W
while True:
W = np.random.random(oscN) * W_MAX + W_MIN
if np.all(np.diff(W) > W_MIN_DIFF): break
R = np.random.random(oscN) * R_MAX + R_MIN
Y0 = np.random.random(oscN) * Y_MAX + Y_MIN
K = np.random.random((oscN, nH * (oscN - 1))) * K_MAX + K_MIN
K[:] = 0.8 * W[:, None] * K / np.sum(K, axis=1)[:, None]
theta = np.column_stack((W, Y0, R, K))
kursl = KurSL(theta)
_, _, s_gen = kursl.generate(t)
s_gen = np.sum(s_gen, axis=0)
t = t[:-1]
####################################################
## Estimate peaks present in signal
preprocess = Preprocessor(max_osc=oscN, nH=nH)
theta_init = preprocess.compute_prior(t, s_gen)
peaks = theta_init[:, 0] * 0.5 / np.pi
####################################################
## Plot extracted frequencies in spectrum
plt.figure()
freq = np.fft.fftfreq(len(t), dt)