def square_envelope(correlation, g_speed, window_params):
square_envelope = correlation.data**2 + \
np.imag(hilbert(correlation.data))**2
if window_params['plot']:
plot_window(correlation, square_envelope, 'N/A')
return square_envelope
def log_en_ratio(correlation, g_speed, window_params):
delta = correlation.stats.delta
window = get_window(correlation.stats, g_speed, window_params)
win = window[0]
if window[2]:
E_plus = np.trapz((correlation.data * win)**2) * delta
E_minus = np.trapz((correlation.data * win[::-1])**2) * delta
msr = log(E_plus / (E_minus + np.finfo(E_minus).tiny))
if window_params['plot']:
plot_window(correlation, win, msr)
else:
msr = np.nan
return msr
def log_en_ratio(correlation, g_speed, window_params):
delta = correlation.stats.delta
window = get_window(correlation.stats, g_speed, window_params)
win = window[0]
data = my_centered(correlation.data, correlation.stats.npts)
if window[2]:
#E_plus = np.trapz((correlation.data * win)**2) * delta
#E_minus = np.trapz((correlation.data * win[::-1])**2) * delta
sig_c = correlation.data * win
sig_a = correlation.data * win[::-1]
E_plus = np.trapz(np.power(sig_c, 2)) * delta
E_minus = np.trapz(np.power(sig_a, 2)) * delta
msr = log(E_plus / E_minus) #+np.finfo(E_minus).tiny))
if window_params['plot']:
plot_window(correlation, win, msr)
else:
msr = np.nan
return msr
def energy(correlation, g_speed, window_params):
window = get_window(correlation.stats, g_speed, window_params)
msr = [np.nan, np.nan]
win = window[0]
if window[2]:
# causal
E = np.trapz((correlation.data * win)**2)
msr[0] = E
if window_params['plot']:
plot_window(correlation, win, E)
# acausal
win = win[::-1]
E = np.trapz((correlation.data * win)**2)
msr[1] = E
if window_params['plot']:
plot_window(correlation, win, E)
return np.array(msr)
def inst_mf(corr_obs, corr_syn, g_speed, window_params):
window = get_window(corr_obs.stats, g_speed, window_params)
win = window[0]
if window[2]:
sig1 = corr_obs.data * (win + win[::-1])
sig2 = corr_syn.data * (win + win[::-1])
# phase misfit .. try instantaneous phase
# hilbert gets the analytic signal (only the name is confusing)
a1 = hilbert(sig1)
a2 = hilbert(sig2)
cc = a1 * np.conjugate(a2)
boxc = np.clip((win + win[::-1]), 0, 1)
dphase = 0.5 * np.trapz(np.angle(cc * boxc)**2)
if window_params['plot']:
plot_window(corr_obs, win, dphase)
else:
dphase = np.nan
return dphase