def update(val):
hurst_related_exponent = hurst_related_exp_slider.val
characteristic_dist = characteristic_dist_slider.val
von_karman_energy_spectrum = mg.calc_von_karman_energy_spectrum(list_wave_number,
hurst_related_exponent,
characteristic_dist)
l.set_ydata(von_karman_energy_spectrum)
hurst_related_exponent_two = hurst_related_exp_slider_two.val
characteristic_dist_two = characteristic_dist_slider_two.val
von_karman_energy_spectrum_two = mg.calc_von_karman_energy_spectrum(list_wave_number,
hurst_related_exponent_two,
characteristic_dist_two)
l_two.set_ydata(von_karman_energy_spectrum_two)
draw()
# generate fft well log
half_num_value_well_log_plus_one = np.floor(num_value_well_log) / 2 + 1
list_wave_number_true = (np.arange(half_num_value_well_log_plus_one)
/ num_value_well_log)
# --> generate noise
gaussian_noise = np.random.normal(mean_gaussian_noise_time_domain,
variance_gaussian_noise_time_domain_true,
num_value_well_log)
fft_gaussian_noise = np.fft.fft(gaussian_noise)
fft_gaussian_noise_truncate = fft_gaussian_noise[0:half_num_value_well_log_plus_one]
if no_noise:
fft_gaussian_noise_truncate = 1
von_karman_energy_spectrum_true = mg.calc_von_karman_energy_spectrum(list_wave_number_true,
hurst_related_exponent_true,
characteristic_dist_true)
fft_well_log_true = np.multiply(np.sqrt(von_karman_energy_spectrum_true), fft_gaussian_noise_truncate)
# calculate the true likelihood
params = (fft_well_log_true, list_wave_number_true)
inverse_likelihood_true = likelihood_measure.calc_inverse_likelihood(
(hurst_related_exponent_true, characteristic_dist_true),
*params)
unique_to_function_call_logger.info("----> true values")
unique_to_function_call_logger.info('b_true = %g, H_true = %g, inverse_likelihood_true = %g,' %
(characteristic_dist_true,
hurst_related_exponent_true,
inverse_likelihood_true))
import model_generation as mg
if __name__ == '__main__':
from pylab import *
from matplotlib.widgets import Slider, Button, RadioButtons
ax = subplot(111)
subplots_adjust(left=0.25, bottom=0.50)
support_start = 0.0
support_stop = 1.0
support_step = 0.001
list_wave_number = arange(support_start, support_stop, support_step)
hurst_related_exponent_initial = 0.5
characteristic_dist_initial = 3
von_karman_energy_spectrum = mg.calc_von_karman_energy_spectrum(list_wave_number,
hurst_related_exponent_initial,
characteristic_dist_initial)
l, = plot(list_wave_number, von_karman_energy_spectrum, lw=2, color='red')
l_two, = plot(list_wave_number, von_karman_energy_spectrum, lw=2, color='blue')
axis([support_start, support_stop, -10, 10])
axcolor = 'lightgoldenrodyellow'
axis_character_dist = axes([0.25, 0.10, 0.65, 0.03], axisbg=axcolor)
axis_hurst_related_exp = axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
axis_character_dist_two = axes([0.25, 0.20, 0.65, 0.03], axisbg=axcolor)
axis_hurst_related_exp_two = axes([0.25, 0.25, 0.65, 0.03], axisbg=axcolor)
hurst_related_exp_min = -0.25
hurst_related_exp_max = 1
characteristic_dist_min = 0
