def test_windows():
array1 = np.zeros(7, dtype=np.int)
array2 = np.zeros(8, dtype=np.int)
array1[3] = 1
array2[4] = 1
assert(my_centered(array1, 5))[2] == 1
assert(my_centered(array2, 5))[2] == 1
assert(my_centered(array1, 9))[4] == 1
assert(my_centered(array2, 9))[4] == 1
tr = Trace()
tr.stats.sac = {}
tr.stats.sac['dist'] = 3.0
tr.data = my_centered(array1, 15) + 1
params = {}
params['hw'] = 1
params['sep_noise'] = 0
params['win_overlap'] = True
params['wtype'] = 'hann'
params['causal_side'] = True
win = get_window(tr.stats, g_speed=1.0, params=params)
assert(len(win) == 3)
assert(pytest.approx(win[0][10]) == 1.0)
snr = snratio(tr, g_speed=1.0, window_params=params)
assert(int(snr) == 1)
def windowed_waveform(correlation, g_speed, window_params):
window = get_window(correlation.stats, g_speed, window_params)
win = window[0]
if window[2]:
win_caus = (correlation.data * win)
win_acaus = (correlation.data * win[::-1])
msr = win_caus + win_acaus
else:
msr = win - win + np.nan
return msr
def energy(corr_o, corr_s, g_speed, window_params):
success = False
window = wn.get_window(corr_o.stats, g_speed, window_params)
win = window[0]
if window[2]:
u1 = 2 * np.multiply(np.power(win, 2), corr_s.data)
u2 = 2 * np.multiply(np.power(win[::-1], 2), corr_s.data)
adjt_src = [u1, u2]
success = True
else:
adjt_src = [win - win + np.nan, win - win + np.nan]
return adjt_src, success
def windowed_waveform(corr_o, corr_s, g_speed, window_params):
success = False
window = wn.get_window(corr_o.stats, g_speed, window_params)
win = window[0] + window[0][::-1]
if window[2]:
u_s = np.multiply(win, corr_s.data)
u_o = np.multiply(win, corr_o.data)
adjt_src = np.multiply(win, (u_s - u_o))
success = True
else:
adjt_src = win - win + np.nan
return adjt_src, success
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]:
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 log_en_ratio_adj(corr_o, corr_s, g_speed, window_params):
success = False
window = wn.get_window(corr_o.stats, g_speed, window_params)
win = window[0]
wn.my_centered(corr_s.data, corr_o.stats.npts)
if window[2]:
sig_c = corr_s.data * win
sig_a = corr_s.data * win[::-1]
E_plus = np.trapz(np.power(sig_c, 2)) * corr_s.stats.delta
E_minus = np.trapz(np.power(sig_a, 2)) * corr_s.stats.delta
# to win**2
u_plus = sig_c * win
u_minus = sig_a * win[::-1]
adjt_src = 2. * (u_plus / E_plus - u_minus / E_minus)
success = True
else:
adjt_src = win - win + np.nan
return adjt_src, success