# ============================================================================== # # mainwindow.py - AcouSed # # Copyright (C) 2024 INRAE # # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see . # # by Brahim MOUDJED # # ============================================================================== # # -*- coding: utf-8 -*- import logging import numpy as np import settings as stg from Model.GrainSizeTools import demodul_granulo, mix_gaussian_model from tools import trace logger = logging.getLogger("acoused") class AcousticInversionMethodHighConcentration(): """ Thi class compute acoustic inversion method adapted for high suspended sediments concentration For instance, the case of dam flush downstream Rhone-Isere confluence (07/01/2018) evaluated at ~ 10g/L """ def __init__(self): pass # ========================================== # Functions # ========================================== # ---------- Computing sound speed ------------- # def water_velocity(self, T): """Computing sond speed from Bilaniuk and Wong 1993""" C = 1.40238744 * 1e3 + 5.03836171 * T - 5.81172916 * 1e-2 * T ** 2 + 3.34638117 * 1e-4 * T ** 3 - \ 1.48259672 * 1e-6 * T ** 4 + 3.16585020 * 1e-9 * T ** 5 return C # -------- Computing water attenuation coefficient ----------- # def water_attenuation(self, freq, T): """Computing attenuation from François and Garrison 1982""" if T > 20: alpha = (3.964 * 1e-4 - 1.146 * 1e-5 * T + 1.45 * 1e-7 * T ** 2 - 6.5 * 1e-10 * T ** 3) * 1e-3 * \ (np.log(10) / 20) * (freq * 1e-3) ** 2 else: alpha = (4.937 * 1e-4 - 2.59 * 1e-5 * T + 9.11 * 1e-7 * T ** 2 - 1.5 * 1e-8 * T ** 3) * 1e-3 * \ (np.log(10) / 20) * (freq * 1e-3) ** 2 return alpha # --- Gaussian mixture --- def compute_particle_size_distribution_in_number_of_particles(self, num_sample, r_grain, frac_vol_cumul): min_demodul = 1e-6 max_demodul = 500e-6 sample_demodul = demodul_granulo(r_grain, frac_vol_cumul[num_sample], min_demodul, max_demodul) resampled_log_array = np.log(np.logspace(-10, -2, 3000)) proba_vol_demodul = mix_gaussian_model(resampled_log_array, sample_demodul.demodul_data_list[2].mu_list, sample_demodul.demodul_data_list[2].sigma_list, sample_demodul.demodul_data_list[2].w_list) proba_vol_demodul = proba_vol_demodul / np.sum(proba_vol_demodul) ss = np.sum(proba_vol_demodul / np.exp(resampled_log_array) ** 3) proba_num = proba_vol_demodul / np.exp(resampled_log_array) ** 3 / ss return proba_num # ------------- Computing ks ------------- # def form_factor_function_MoateThorne2012(self, a, freq, C): """This function computes the form factor based on the equation of Moate and Thorne (2012)""" # computing the wave number k = 2 * np.pi * freq / C x = k * a f = (x ** 2 * (1 - 0.25 * np.exp(-((x - 1.5) / 0.35) ** 2)) * (1 + 0.6 * np.exp(-((x - 2.9) / 1.15) ** 2))) / ( 42 + 28 * x ** 2) return f def ks(self, proba_num, freq, C): # --- Compute k_s by dividing two integrals --- resampled_log_array = np.log(np.logspace(-10, -2, 3000)) a2f2pdf = 0 a3pdf = 0 for i in range(len(resampled_log_array)): a = np.exp(resampled_log_array)[i] a2f2pdf += a**2 * self.form_factor_function_MoateThorne2012(a, freq, C)**2 * proba_num[i] a3pdf += a**3 * proba_num[i] ks = np.sqrt(a2f2pdf / a3pdf) return ks # ------------- Computing sv ------------- # def sv(self, ks, M_sand): sv = (3 / (16 * np.pi)) * (ks ** 2) * M_sand return sv # ------------- Computing X ------------- # def X_exponent(self, freq1, freq2, sv_freq1, sv_freq2): X = np.log(sv_freq1 / sv_freq2) / np.log(freq1 / freq2) return X # -------------- Computing Kt -------------- # def kt_corrected(self, r, water_velocity, RxGain, TxGain, kt_ref): """Computing the instrument constant Kt that depends on gain and temperature""" # Cell size delta_r = r[1] - r[0] # Pulse length tau = 2 * delta_r / 1500 # Sound speed cel = water_velocity # Reference pulse length tau_ref = 13.33333e-6 # Reference sound speed c_ref = 1475 # Gain gain = 10 ** ((RxGain + TxGain) / 20) # Computing Kt kt = kt_ref * gain * np.sqrt(tau * cel / (tau_ref * c_ref)) # 1D numpy array return kt # ------------- Computing J_cross_section ------------- # def j_cross_section(self, BS, r2D, kt): J_cross_section = (3 / (16 * np.pi)) * ((BS**2 * r2D**2) / kt**2) return J_cross_section # ------------- Computing alpha_s ------------- # def alpha_s(self, sv, j_cross_section, depth, alpha_w): alpha_s = (np.log(sv / j_cross_section) / (4 * depth)) - alpha_w return alpha_s # ------------- Computing interpolation of fine SSC ------------- def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom): '''Computing interpolation of fine SSC data obtained from water sampling collected at various depth in the vertical sample''' res = np.zeros((len(range_cells),)) * np.nan l0 = sample_depth l1 = [l0.index(x) for x in sorted(l0)] l2 = [l0[k] for k in l1] c1 = [list(M_profile)[j] for j in l1] for i in range(len(c1) - 1): # print("i = ", i) r_ini = l2[i] c_ini = c1[i] r_end = l2[i + 1] c_end = c1[i + 1] # Computing the linear equation a = (c_end - c_ini) / (r_end - r_ini) b = c_ini - a * r_ini # Finding the indices of r_ini and r_end in the interpolated array loc = (range_cells >= r_ini) * (range_cells < r_end) # Filling the array with interpolation values res[loc] = range_cells[loc] * a + b # Filling first and last values i = 0 while np.isnan(res[i]): res[i] = c1[0] i += 1 # Filling the last values i = -1 while np.isnan(res[i]): res[i] = c1[-1] i += -1 if r_bottom.size != 0: res[np.where(range_cells > r_bottom)] = np.nan loc_point_lin_interp0 = range_cells[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))] res0 = res[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))] loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > l2[0])] res = res0[np.where(loc_point_lin_interp0 > l2[0])] return (loc_point_lin_interp, res) # ------------- Computing zeta ------------- # def zeta(self, alpha_s, r, M_profile_fine): delta_r = r[1] - r[0] zeta = alpha_s / (np.sum(np.array(M_profile_fine)*delta_r)) return zeta # ------------- Computing VBI ------------- # def VBI_cross_section(self, freq1, freq2, zeta_freq1, zeta_freq2, j_cross_section_freq1, j_cross_section_freq2, r2D, water_attenuation_freq1, water_attenuation_freq2, X): logVBI = ((zeta_freq2 * np.log(j_cross_section_freq1 * np.exp(4 * r2D * water_attenuation_freq1) / (freq1 ** X)) - zeta_freq1 * np.log(j_cross_section_freq2 * np.exp(4 * r2D * water_attenuation_freq2) / (freq2 ** X))) / (zeta_freq2 - zeta_freq1)) return np.exp(logVBI) # ------------- Computing SSC fine ------------- # def SSC_fine(self, zeta, r2D, VBI, freq, X, j_cross_section, alpha_w): SSC_fine = (1/zeta) * ( 1/(4 * r2D) * np.log((VBI * freq**X) / j_cross_section) - alpha_w) return SSC_fine # ------------- Computing SSC sand ------------- # def SSC_sand(self, VBI, freq, X, ks): SSC_sand = (16 * np.pi * VBI * freq ** X) / (3 * ks**2) return SSC_sand