def calc_inverse_likelihood(self,
hurst_chardist_opt_variables,
*params):
hurst_related_exponent, characteristic_dist = hurst_chardist_opt_variables
fft_well_log, list_wave_number = params
log_abs_fft_noise = mg.calc_log_abs_fft_noise(fft_well_log,
list_wave_number,
hurst_related_exponent,
characteristic_dist)
half_num_value = np.floor(log_abs_fft_noise.size / 2)
first_half_median = np.median(log_abs_fft_noise[:half_num_value])
second_half_median = np.median(log_abs_fft_noise[half_num_value + 1:])
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
out = np.abs(first_half_median - second_half_median)
if len(w):
# do something with the first warning
print w[0].message
print "hurst_related_exponent = %g, characteristic_dist=%g" % (hurst_related_exponent,
characteristic_dist)
print "first_half_median = %g, second_half_median=%g" % (first_half_median, second_half_median)
raise
return out
def calc_inverse_likelihood(self,
hurst_chardist_opt_variables,
*params):
hurst_related_exponent, characteristic_dist = hurst_chardist_opt_variables
fft_well_log, list_wave_number = params
log_abs_fft_noise = mg.calc_log_abs_fft_noise(fft_well_log,
list_wave_number,
hurst_related_exponent,
characteristic_dist)
return np.var(log_abs_fft_noise)
def calc_inverse_likelihood(self,
hurst_chardist_opt_variables,
*params):
hurst_related_exponent, characteristic_dist = hurst_chardist_opt_variables
fft_well_log, list_wave_number = params
log_abs_fft_noise = mg.calc_log_abs_fft_noise(fft_well_log,
list_wave_number,
hurst_related_exponent,
characteristic_dist)
num_fft_well_log = fft_well_log.size
# If noise is zero, log_abs_fft_noise = 0 vector,
# We prevent division of zero by adding non-zero constant to log_abs_fft_noise.
log_abs_fft_noise = log_abs_fft_noise + 1
sum_log_abs_fft_noise = np.sum(log_abs_fft_noise)
semblance = sum_log_abs_fft_noise / (np.sqrt(sum_log_abs_fft_noise) * np.sqrt(num_fft_well_log))
return np.abs(semblance - 1)
