acoustic inversion method high concentration file is cleaned from useless commented lines and useless print
brahim
parent
cea9e35498
commit
65a9422f71
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@ -21,7 +21,6 @@
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# -*- coding: utf-8 -*-
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import matplotlib.pyplot as plt
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import numpy as np
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import settings as stg
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from Model.GrainSizeTools import demodul_granulo, mix_gaussian_model
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@ -58,17 +57,6 @@ class AcousticInversionMethodHighConcentration():
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(np.log(10) / 20) * (freq * 1e-3) ** 2
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return alpha
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# ---------- Conmpute FBC ----------
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# def compute_FCB(self):
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# # print(self.BS_averaged_cross_section_corr.V.shape)
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# # print(self.r_2D.shape)
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# FCB = np.zeros((256, 4, 1912))
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# for f in range(4):
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# # print(self.alpha_w_function(self.Freq[f], self.temperature))
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# FCB[:, f, :] = np.log(self.BS_averaged_cross_section_corr.V[:, f, :]) + np.log(self.r_3D[:, f, :]) + \
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# np.log(2 * self.alpha_w_function(self.Freq[f], self.temperature) * self.r_3D[:, f, :])
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# return FCB
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# --- Gaussian mixture ---
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def compute_particle_size_distribution_in_number_of_particles(self, num_sample, r_grain, frac_vol_cumul):
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min_demodul = 1e-6
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@ -82,15 +70,6 @@ class AcousticInversionMethodHighConcentration():
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sample_demodul.demodul_data_list[2].sigma_list,
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sample_demodul.demodul_data_list[2].w_list)
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# N_modes = 3
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# sample_demodul.print_mode_data(N_modes)
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# sample_demodul.plot_interpolation()
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# sample_demodul.plot_modes(N_modes)
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# print(f"mu_list : {sample_demodul.demodul_data_list[3 - 1].mu_list}")
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# print(f"sigma_list : {sample_demodul.demodul_data_list[3 - 1].sigma_list}")
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# print(f"w_list : {sample_demodul.demodul_data_list[3 - 1].w_list}")
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proba_vol_demodul = proba_vol_demodul / np.sum(proba_vol_demodul)
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ss = np.sum(proba_vol_demodul / np.exp(resampled_log_array) ** 3)
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proba_num = proba_vol_demodul / np.exp(resampled_log_array) ** 3 / ss
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@ -106,23 +85,9 @@ class AcousticInversionMethodHighConcentration():
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x = k * a
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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))) / (
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42 + 28 * x ** 2)
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# print(f"form factor = {f}")
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return f
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# def ks(self, num_sample_sand, radius_grain_sand, frac_vol_sand_cumul, freq, C):
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def ks(self, proba_num, freq, C):
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# --- Calcul de la fonction de form ---
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# form_factor = self.form_factor_function_MoateThorne2012(a, freq)
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# print(f"form_factor shape = {form_factor}")
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# print(f"form_factor = {form_factor}")
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#--- Particle size distribution ---
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# proba_num = (
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# self.compute_particle_size_distribution_in_number_of_particles(
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# num_sample=num_sample_sand, r_grain=radius_grain_sand, frac_vol_cumul=frac_vol_sand_cumul[num_sample_sand]))
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# print(f"proba_num : {proba_num}")
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# --- Compute k_s by dividing two integrals ---
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resampled_log_array = np.log(np.logspace(-10, -2, 3000))
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a2f2pdf = 0
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@ -132,28 +97,17 @@ class AcousticInversionMethodHighConcentration():
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a2f2pdf += a**2 * self.form_factor_function_MoateThorne2012(a, freq, C)**2 * proba_num[i]
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a3pdf += a**3 * proba_num[i]
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# print("form factor ", self.form_factor_function_MoateThorne2012(a, freq, C))
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# print(f"a2f2pdf = {a2f2pdf}")
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# print(f"a3pdf = {a3pdf}")
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ks = np.sqrt(a2f2pdf / a3pdf)
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# ks = np.array([0.04452077, 0.11415143, 0.35533713, 2.47960051])
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# ks = ks0[ind]
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return ks
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# ------------- Computing sv ------------- #
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def sv(self, ks, M_sand):
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# print(f"ks = {ks}")
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# print(f"M_sand = {M_sand}")
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sv = (3 / (16 * np.pi)) * (ks ** 2) * M_sand
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# sv = np.full((stg.r.shape[1], stg.t.shape[1]), sv0)
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return sv
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# ------------- Computing X ------------- #
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def X_exponent(self, freq1, freq2, sv_freq1, sv_freq2):
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# X0 = [3.450428714146802, 3.276478927777019, 3.6864638665972893, 0]
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# X = X0[ind]
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X = np.log(sv_freq1 / sv_freq2) / np.log(freq1 / freq2)
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return X
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@ -174,165 +128,43 @@ class AcousticInversionMethodHighConcentration():
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gain = 10 ** ((RxGain + TxGain) / 20)
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# Computing Kt
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kt = kt_ref * gain * np.sqrt(tau * cel / (tau_ref * c_ref)) # 1D numpy array
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# kt = np.reshape(kt0, (1, 2)) # convert to 2d numpy array to compute J_cross_section
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# print(f"kt = {kt}")
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# kt_2D = np.repeat(np.array([kt]), stg.r.shape[1], axis=0)
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# print("kt 2D ", kt_2D)
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# print("kt 2D shape ", kt_2D.shape)
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# # kt_3D = np.zeros((kt_2D.shape[1], kt_2D.shape[0], stg.t.shape[1]))
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# # for k in range(kt_2D.shape[1]):
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# # kt_3D[k, :, :] = np.repeat(kt_2D, stg.t.shape[1], axis=1)[:, k * stg.t.shape[1]:(k + 1) * stg.t.shape[1]]
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# kt_3D = np.repeat(kt_2D.transpose()[:, :, np.newaxis], stg.t.shape[1], axis=2)
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# # print("kt 3D ", kt_3D)
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# print("kt 3D shape ", kt_3D.shape)
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return kt
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# ------------- Computing J_cross_section ------------- #
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def j_cross_section(self, BS, r2D, kt):
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# J_cross_section = np.zeros((1, BS.shape[1], BS.shape[2])) # 2 because it's a pair of frequencies
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# print("BS.shape", BS.shape)
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# print("r2D.shape", r2D.shape)
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# print("kt.shape", kt.shape)
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# if stg.ABS_name == "Aquascat 1000R":
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# print("--------------------------------")
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# print("BS : ", BS)
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# print("BS min : ", np.nanmin(BS))
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# print("BS max : ", np.nanmax(BS))
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# print("r2D : ", r2D)
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# print("kt shape : ", kt.shape)
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# print("kt : ", kt)
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# print("--------------------------------")
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# for k in range(1):
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# J_cross_section[k, :, :] = (3 / (16 * np.pi)) * ((BS[k, :, :]**2 * r2D[k, :, :]**2) / kt[k, :, :]**2)
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J_cross_section = (3 / (16 * np.pi)) * ((BS**2 * r2D**2) / kt**2)
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# J_cross_section[J_cross_section == 0] = np.nan
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# print("J_cross_section.shape", J_cross_section.shape)
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# elif stg.ABS_name == "UB-SediFlow":
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# for k in range(1):
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# J_cross_section[k, :, :] = (3 / (16 * np.pi)) * ((BS[k, :, :]**2 * r2D[0, :, :]**2) / kt[k, :, :]**2)
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# print("compute j_cross_section finished")
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return J_cross_section
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# ------------- Computing alpha_s ------------- #
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def alpha_s(self, sv, j_cross_section, depth, alpha_w):
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alpha_s = (np.log(sv / j_cross_section) / (4 * depth)) - alpha_w
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print("----------------------------")
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print(f"sv = {sv}")
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print(f"j_cross_section = {j_cross_section}")
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print(f"depth = {depth}")
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print(f"alpha_w = {alpha_w}")
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print(f"(np.log(sv / j_cross_section) / (4 * depth)) = {(np.log(sv / j_cross_section) / (4 * depth))}")
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print(f"alpha_s {alpha_s}")
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return alpha_s
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# ------------- Computing interpolation of fine SSC data obtained from water sampling -------------
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# ------------- collected at various depth in the vertical sample -------------
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# def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom):
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# res = np.zeros((len(range_cells),)) * np.nan
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# for i in range(len(M_profile) - 1):
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# # print(f"i = {i}")
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# r_ini = sample_depth[i]
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# # print(f"r_ini = {r_ini}")
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# c_ini = M_profile[i]
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# # print(f"c_ini = {c_ini}")
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# r_end = sample_depth[i + 1]
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# # print(f"r_end = {r_end}")
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# c_end = M_profile[i + 1]
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# # print(f"c_end = {c_end}")
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#
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# # Computing the linear equation
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# a = (c_end - c_ini) / (r_end - r_ini)
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# # print(f"a = {a}")
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# b = c_ini - a * r_ini
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# # print(f"b = {b}")
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#
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# # Finding the indices of r_ini and r_end in the interpolated array
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# # print(f"range_cells = {range_cells}")
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# loc = (range_cells >= r_ini) * (range_cells < r_end)
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# # print(f"loc = {loc}")
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# # print(f"loc shape = {len(loc)}")
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#
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# # Filling the array with interpolation values
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# res[loc] = range_cells[loc] * a + b
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# # print(res.shape)
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# # print(f"res = {res}")
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# # print(f"1. res.shape = {res.shape}")
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#
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# # Filling first and last values
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# i = 0
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# while np.isnan(res[i]):
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# res[i] = M_profile[0]
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# i += 1
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#
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# # Filling the last values
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# i = -1
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# while np.isnan(res[i]):
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# res[i] = M_profile[-1]
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# i += -1
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# # print(f"res.shape = {res.shape}")
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# # print(f"res = {res}")
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# # print(f"r_bottom.shape = {r_bottom.shape}")
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# # print(f" = {res}")
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#
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# if r_bottom.shape != (0,):
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# res[np.where(range_cells > r_bottom)] = np.nan
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#
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# loc_point_lin_interp0 = range_cells[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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# # print(f"range_cells : {range_cells}")
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# # print(f"loc_point_lin_interp0 shape : {len(loc_point_lin_interp0)}")
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# # print(f"loc_point_lin_interp0 : {loc_point_lin_interp0}")
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# res0 = res[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
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#
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# loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > range_cells[0])]
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# # print(f"loc_point_lin_interp shape : {len(loc_point_lin_interp)}")
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# # print(f"loc_point_lin_interp : {loc_point_lin_interp}")
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# res = res0[np.where(loc_point_lin_interp0 > range_cells[0])]
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#
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# # fig, ax = plt.subplots(nrows=1, ncols=1)
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# # ax.plot(loc_point_lin_interp, res[:len(loc_point_lin_interp)], marker="*", mfc="blue")
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# # ax.plot(sample_depth, M_profile, marker="o", mfc="k", mec="k")
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# # plt.show()
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#
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# return (loc_point_lin_interp, res)
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# ------------- Computing interpolation of fine SSC -------------
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def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom):
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'''Computing interpolation of fine SSC data obtained from water sampling
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collected at various depth in the vertical sample'''
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res = np.zeros((len(range_cells),)) * np.nan
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print("range_cells ", range_cells.shape)
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l0 = sample_depth
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print("l0 = ", l0)
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l1 = [l0.index(x) for x in sorted(l0)]
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print("l1 = ", l1)
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l2 = [l0[k] for k in l1]
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print("l2 = ", l2)
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c1 = [list(M_profile)[j] for j in l1]
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print("c1 = ", c1)
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for i in range(len(c1) - 1):
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# print("i = ", i)
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r_ini = l2[i]
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c_ini = c1[i]
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r_end = l2[i + 1]
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c_end = c1[i + 1]
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print("r_ini ", r_ini, "c_ini ", c_ini, "r_end ", r_end, "c_end ", c_end)
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# Computing the linear equation
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a = (c_end - c_ini) / (r_end - r_ini)
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b = c_ini - a * r_ini
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print("range_cells ", (range_cells))
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# Finding the indices of r_ini and r_end in the interpolated array
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loc = (range_cells >= r_ini) * (range_cells < r_end)
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print("range_cells >= r_ini ", range_cells >= r_ini)
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print("range_cells < r_end ", range_cells < r_end)
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print("loc ", loc)
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# Filling the array with interpolation values
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res[loc] = range_cells[loc] * a + b
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print("a = ", a, "b = ", b)
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print("res ", res)
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# Filling first and last values
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i = 0
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while np.isnan(res[i]):
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@ -346,9 +178,6 @@ class AcousticInversionMethodHighConcentration():
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i += -1
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if r_bottom.size != 0:
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print("res ", res.shape)
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print("range_cells ", len(range_cells))
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# print("r_bottom ", len(r_bottom))
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res[np.where(range_cells > r_bottom)] = np.nan
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loc_point_lin_interp0 = range_cells[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))]
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@ -357,13 +186,6 @@ class AcousticInversionMethodHighConcentration():
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loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > l2[0])]
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res = res0[np.where(loc_point_lin_interp0 > l2[0])]
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# fig, ax = plt.subplots(nrows=1, ncols=1)
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# ax.plot(res[:len(loc_point_lin_interp)], -loc_point_lin_interp, marker="*", mfc="blue")
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# ax.plot(c1, [-x for x in l2], marker="o", mfc="k", mec="k", ls="None")
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# ax.set_xlabel("Concentration (g/L)")
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# ax.set_ylabel("Depth (m)")
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# plt.show()
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return (loc_point_lin_interp, res)
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# ------------- Computing zeta ------------- #
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@ -372,39 +194,6 @@ class AcousticInversionMethodHighConcentration():
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delta_r = r[1] - r[0]
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zeta = alpha_s / (np.sum(np.array(M_profile_fine)*delta_r))
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# print(f"np.sum(M_profile_fine*delta_r) : {np.sum(M_profile_fine*delta_r)}")
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# zeta0 = np.array([0.021, 0.035, 0.057, 0.229])
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# zeta = zeta0[ind]
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# zeta0 = np.array([0.04341525, 0.04832906, 0.0847188, np.nan])
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# zeta = zeta0[[ind1, ind2]]
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# for k in range(3):
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# for p in range(3):
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# if np.isnan(ind_X_min_around_sample[p, k]):
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# zeta_list_exp.append(np.nan)
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# else:
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# ind_X_min = int(ind_X_min_around_sample[p, k])
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# ind_X_max = int(ind_X_max_around_sample[p, k])
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# ind_r_min = int(ind_r_min_around_sample[p, k])
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# ind_r_max = int(ind_r_max_around_sample[p, k])
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#
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# R_temp = R_cross_section[ind_r_min:ind_r_max, :, ind_X_min:ind_X_max]
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# J_temp = J_cross_section[ind_r_min:ind_r_max, :, ind_X_min:ind_X_max]
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# aw_temp = aw_cross_section[ind_r_min:ind_r_max, :, ind_X_min:ind_X_max]
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# sv_temp_1 = np.repeat([sv_list_temp[3 * k + p]], np.shape(R_temp)[0], axis=0)
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# sv_temp = np.swapaxes(np.swapaxes(np.repeat([sv_temp_1], np.shape(R_temp)[2], axis=0), 1, 0), 2, 1)
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# ind_depth = np.where(R_cross_section[:, 0, 0] >= M_list_temp[k][0, p + 1])[0][0]
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# # Using concentration profile
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# zeta_temp = alpha_s / ((1 / M_list_temp[k][0, p + 1]) * (R_cross_section[0, 0, 0] * M_list_temp[k][1, 0] +
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# delta_r * np.sum(M_interpolate_list[k][:ind_depth])))
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# zeta_temp = (1 / (4 * R_temp) *
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# np.log(sv_temp / J_temp) - aw_temp) / ((1 / M_list_temp[k][0, p + 1]) *
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# (R_cross_section[0, 0, 0] * M_list_temp[k][
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# 1, 0] +
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# delta_r * np.sum(
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# M_interpolate_list[k][:ind_depth])))
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# zeta_list_exp.append(np.mean(np.mean(zeta_temp, axis=0), axis=1))
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return zeta
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# ------------- Computing VBI ------------- #
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@ -415,21 +204,6 @@ class AcousticInversionMethodHighConcentration():
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water_attenuation_freq1, water_attenuation_freq2,
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X):
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# print('self.zeta_exp[ind_j].shape', self.zeta_exp[ind_j])
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# print('np.log(self.j_cross_section[:, ind_i, :]).shape', np.log(self.j_cross_section[:, ind_i, :]).shape)
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# print('self.r_3D[:, ind_i, :]', self.r_3D[:, ind_i, :].shape)
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# print('self.water_attenuation[ind_i]', self.water_attenuation[ind_i])
|
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# print('self.x_exp[0.3-1 MHz]', self.x_exp['0.3-1 MHz'].values[0])
|
||||
# print("start computing VBI")
|
||||
# print("================================")
|
||||
# print(f"zeta_freq2 : {zeta_freq2}")
|
||||
# print(f"j_cross_section_freq1 : {j_cross_section_freq1.shape}")
|
||||
# print(f"r2D : {r2D.shape}")
|
||||
# print(f"water_attenuation_freq1 : {water_attenuation_freq1}")
|
||||
# print(f"freq1 : {freq1}")
|
||||
# print(f"X : {X}")
|
||||
# print("================================")
|
||||
|
||||
logVBI = ((zeta_freq2 *
|
||||
np.log(j_cross_section_freq1 * np.exp(4 * r2D * water_attenuation_freq1) /
|
||||
(freq1 ** X)) -
|
||||
|
|
@ -438,31 +212,16 @@ class AcousticInversionMethodHighConcentration():
|
|||
(freq2 ** X))) /
|
||||
(zeta_freq2 - zeta_freq1))
|
||||
|
||||
# logVBI = (freq2**2 * np.log(j_cross_section_freq1 / freq1**X) -
|
||||
# freq1**2 * np.log(j_cross_section_freq2 / freq2**X)) / (freq2**2 - freq1**2)
|
||||
|
||||
# logVBI = (( np.full((stg.r.shape[1], stg.t.shape[1]), zeta_freq2) *
|
||||
# np.log(j_cross_section_freq1 * np.exp(4 * r2D * np.full((stg.r.shape[1], stg.t.shape[1]), water_attenuation_freq1)) /
|
||||
# (freq1 ** X)) -
|
||||
# np.full((stg.r.shape[1], stg.t.shape[1]), zeta_freq1) *
|
||||
# np.log(j_cross_section_freq2 * np.exp(4 * r2D * np.full((stg.r.shape[1], stg.t.shape[1]), water_attenuation_freq2)) /
|
||||
# (freq2 ** X))) /
|
||||
# (zeta_freq2 - zeta_freq1))
|
||||
|
||||
print("compute VBI finished")
|
||||
|
||||
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)
|
||||
print("compute SSC fine finished")
|
||||
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)
|
||||
print("compute SSC sand finished")
|
||||
return SSC_sand
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue