time noise and depth noise variable are used instead of time and depth to avoid size incompatibility #9

Model/acoustic_data_loader.py

@@ -5,35 +5,25 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib.colors import LogNorm
 
-# path_BS_raw_data = "/home/bmoudjed/Documents/2 Data/Confluence_Rhône_Isere_2018/Acoustic_data/20180107123500.aqa"
-# path_BS_raw_data = "/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/" \
-#                        "Data/AcousticNoise_data/20180107121600.aqa"
-
 
 class AcousticDataLoader:
 
     def __init__(self, path_BS_raw_data: str):
 
         self.path_BS_raw_data = path_BS_raw_data
-        print(self.path_BS_raw_data)
         # --- Load Backscatter acoustic raw data with RawAquascatData class ---
 
         self._data_BS = RawAquascatData(self.path_BS_raw_data)
-        print(self._data_BS.V.shape)
         self._BS_raw_data = np.swapaxes(self._data_BS.V, 0, 1)
-        print(f"BS raw data shape = {self._BS_raw_data.shape}")
 
         self._freq = self._data_BS.Freq
-        print(f"freq shape = {self._freq.shape}")
         self._freq_text = self._data_BS.freqText
 
         self._r = np.repeat(np.transpose(self._data_BS.r), self._freq.shape[0], axis=0)
-        print(f"r shape = {self._r.shape}")
 
         self._time = np.repeat(
             np.transpose(np.array([t / self._data_BS.PingRate for t in range(self._data_BS.NumProfiles)])[:, np.newaxis]),
             self._freq.shape[0], axis=0)
-        print(f"time shape = {self._time.shape}")
 
         self._date = self._data_BS.date.date()
         self._hour = self._data_BS.date.time()
@@ -48,97 +38,30 @@ class AcousticDataLoader:
         self._gain_rx = self._data_BS.RxGain.tolist()
         self._gain_tx = self._data_BS.TxGain.tolist()
 
-        # print((self._cell_size))
-        # print((self._nb_pings_averaged_per_profile))
-
-        # print(self._r[0, :][1] - self._r[1, :][0])
-        # print(type(self._nb_cells), self._nb_cells)
-
-        # self._snr = np.array([])
-        # self._snr_reshape = np.array([])
-        # self._time_snr = np.array([])
-
-        # print(type(self._gain_tx))
-
-        # print(["BS - " + f for f in self._freq_text])
-        # print(self._time.shape[0]*self._r.shape[0]*4)
-
-        # print(self._time[np.where(np.floor(self._time) == 175)])
-        # print(np.where((self._time) == 155)[0][0])
-
-        # fig, ax = plt.subplots(nrows=1, ncols=1)
-        # # ax.pcolormesh(self._time[0, :2200], -self._r[0, :], (self._BS_raw_data[0, :, :2200]),
-        # #               cmap='viridis',
-        # #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[0, :, :2200])))  # , shading='gouraud')
-        # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[1]), self._BS_raw_data[2, :, :], cmap='viridis',
-        #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
-        # ax.set_xticks([])
-        # ax.set_yticks([])
-        # plt.show()
-
-        # --- Plot vertical profile for bottom detection ---
-        # fig2, ax2 = plt.subplots(nrows=1, ncols=1, layout="constrained")
-        # ax2.plot(self._BS_raw_data[0, :, 1], -self._r[0], "k.-")
-        # plt.show()
-
-        # fig, ax = plt.subplots(nrows=1, ncols=1)
-        # ax.plot(self._BS_raw_data[:, 0, 100] , self._r)
-        # ax.set_ylim(2, 20)
-        # plt.show()
-
-        # print(self.reshape_BS_raw_cross_section()[0, 0])
-        # self.reshape_BS_raw_cross_section()
-        # self.reshape_r()
-        # self.reshape_t()
-        # self.compute_r_2D()
-
     def reshape_BS_raw_data(self):
         BS_raw_cross_section = np.reshape(self._BS_raw_data,
                                           (self._r.shape[1] * self._time.shape[1], self._freq.shape[0]),
                                           order="F")
-        print(BS_raw_cross_section.shape)
         return BS_raw_cross_section
 
     def reshape_r(self):
-        # r = np.reshape(np.repeat(self._r[0, :], self._time.shape[0], axis=1),
-        #                self._r.shape[0]*self._time.shape[0],
-        #                order="F")
         r = np.zeros((self._r.shape[1] * self._time.shape[1], self._freq.shape[0]))
         for i, _ in enumerate(self._freq):
             for j in range(self._time.shape[1]):
 
                 r[j*self._r.shape[1]:(j+1)*self._r.shape[1], i] = self._r[i, :]
-
-            # r[:, i] = np.repeat(self._r[i, :], self._time.shape[1])
-        print(r.shape)
         return r
 
     def compute_r_2D(self):
         r2D = np.zeros((self._freq.shape[0], self._r.shape[1], self._time.shape[1]))
         for f, _ in enumerate(self._freq):
             r2D[f, :, :] = np.repeat(np.transpose(self._r[f, :])[:, np.newaxis], self._time.shape[1], axis=1)
-        print(r2D.shape)
         return r2D
 
     def reshape_t(self):
-        # t = np.reshape(np.repeat(self._time, self._r.shape[0]), (self._time.shape[0]*self._r.shape[0], 1))
         t = np.zeros((self._r.shape[1] * self._time.shape[1], self._freq.shape[0]))
         for i, _ in enumerate(self._freq):
             t[:, i] = np.repeat(self._time[i, :], self._r.shape[1])
-        print(t.shape)
         return t
 
-    # def concatenate_data(self):
-    #     self.reshape_t()
-    #     self.reshape_BS_raw_cross_section()
-    #     # print(self.reshape_t().shape)
-    #     # print(se.lf.reshape_BS_raw_cross_section().shape)
-    #     df = pd.DataFrame(np.concatenate((self.reshape_t(), self.reshape_BS_raw_cross_section()), axis=1),
-    #                       columns=["time"] + self._freq_text)
-    #     return df
-
-
-# if __name__ == "__main__":
-#     AcousticDataLoader(path_BS_raw_data)
-
 

Model/acoustic_inversion_method_high_concentration.py

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

README.md

@@ -1,8 +1,17 @@
 # AcouSed
 
-**TODO** short description
+AcouSed for **Acou**stic Backscattering for Concentration of Suspended **Sed**iments in Rivers is a software developped by INRAE, in collaboation with CNR. 
 
-## Getting started
+![](icons/Logo-INRAE.jpg)
+
+It is divided in six tabs: 
+- Acoustic data : acoustic raw data are downloaded and visualised 
+- Signal preprocessing : acoustic raw signal is preprocessed with filters
+- Sample data : fine and sand sediments samples data are downloaded and visualised 
+- Calibration : calibration parameter are computed
+- Inversion : inversion method is calculated to provide fine and sand sediments fields
+
+## Software documentation
 
 ### Installation
 
@@ -11,8 +20,9 @@ greater. By default, Acoused is developped with Pypi package
 dependencies, but is also possible to use Guix package manager to run
 Acoused.
 
-### **TODO** Windows
+## Development documentation
 
+### **TODO** Windows
 
 ### Linux
 
@@ -39,33 +49,34 @@ script `guix.sh` to run the program.
   guix shell sqlitebrowser -- ./guix.sh
 ```
 
-## **TODO** Documentation
-
-## Authors and acknowledgment
-
-### Development
-
-- Brahim MOUDJED  ????-2025 ([INRAE](https://www.inrae.fr/))
-- Pierre-Antoine ROUBY  2025 ([TECC](https://parouby.fr))
-
-### **TODO** Funding
-
-- [INRAE](https://www.inrae.fr/)
-- CNR
-
 ## License
 
-Copyright (C) ????-2025  INRAE
+AcouSed 
+Copyright (C) 2024 - INRAE 
 
-This program is free software: you can redistribute it and/or modify
+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. 
-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,
+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. 
-but WITHOUT ANY WARRANTY; without even the implied warranty of
+
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+You should have received a copy of the GNU General Public License along with this program.  If not, see <https://www.gnu.org/licenses/>.
-GNU General Public License for more details.
+
+## Authors & Contacts
+
+- Brahim MOUDJED  2022-2025 ([INRAE](https://www.inrae.fr/))
+- Pierre-Antoine ROUBY  2025 ([TECC](https://parouby.fr))
+
+If you have any questions or suggestions, please contact us to celine.berni@inrae.fr and/or jerome.lecoz@inrae.fr.
+
+## Acknowledgment (Funding)
+This study was conducted within the [Rhône Sediment Observatory](https://observatoire-sediments-rhone.fr/) (OSR), a multi-partner research program funded through the Plan Rhône by the European Regional Development Fund (ERDF), Agence de l’Eau RMC, CNR, EDF and three regional councils (Auvergne-Rhône-Alpes, PACA and Occitanie). It was also support by CNR. 
+
+## Support files & References 
+
+- [ ] [Acoustic inversion method diagram](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Acoustic_Inversion_theory.pdf?ref_type=heads) 
+- [ ] [Tutorial AQUAscat software : AQUAtalk](https://forgemia.inra.fr/theophile.terraz/acoused/-/blob/main/Tutorial_AQUAscat_software.pdf?ref_type=heads)
+
+
+- [ ] [Adrien Vergne thesis (2018)](https://theses.fr/2018GREAU046)
+- [ ] [Vergne A., Le Coz J., Berni C., & Pierrefeu G. (2020), Water Resources Research, 56(2)](https://doi.org/10.1029/2019WR024877)
+- [ ] [Vergne A., Berni C., Le Coz J., & Tencé F., (2021), Water Resources Research, 57(9)](https://doi.org/10.1029/2021WR029589)
 
-You should have received a copy of the GNU General Public License
-along with this program.  If not, see <https://www.gnu.org/licenses/>.

View/plot_noise_window.py

@@ -1,8 +1,7 @@
 
 from PyQt5.QtGui import QIcon, QPixmap
 from PyQt5.QtWidgets import (QWidget, QLabel, QHBoxLayout, QVBoxLayout, QApplication, QMainWindow, QGridLayout,
-                             QDialog, QDialogButtonBox, QPushButton, QTextEdit, QFrame, QTabWidget, QScrollArea)
+                             QDialog, QFrame, QTabWidget, QScrollArea)
-from PyQt5.QtCore import Qt
 
 import numpy as np
 
@@ -11,13 +10,8 @@ from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
 from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolBar
 from matplotlib.colors import LogNorm, BoundaryNorm
 
-import datetime
-
 import settings as stg
 
-from Translation.constant_string import HORIZONTAL
-from settings import depth_cross_section
-
 
 class PlotNoiseWindow(QDialog):
 
@@ -55,12 +49,10 @@ class PlotNoiseWindow(QDialog):
                     val_min = np.nanmin(stg.BS_noise_raw_data[i][freq_ind, :, :])
                     val_max = np.nanmax(stg.BS_noise_raw_data[i][freq_ind, :, :])
 
-                    print("val_min = ", val_min, "val_max = ", val_max)
-
                     if val_min == val_max:
                         exec("pcm = self.ax" + str(i) + "[" + str(freq_ind) + "]" + ".pcolormesh(" +
-                             "stg.time[" + str(i) + "][" + str(freq_ind) + ", :]," +
+                             "stg.time_noise[" + str(i) + "][" + str(freq_ind) + ", :]," +
-                             "-stg.depth[" + str(i) + "][" + str(freq_ind) + ", :]," +
+                             "-stg.depth_noise[" + str(i) + "][" + str(freq_ind) + ", :]," +
                              "stg.BS_noise_raw_data[" + str(i) + "][" + str(freq_ind) + ", :, :]," +
                              "cmap='hsv')")
                     else:

View/signal_processing_tab.py

@@ -1084,23 +1084,20 @@ class SignalProcessingTab(QWidget):
                         val_max = np.nanmax(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
                         if val_min == val_max:
                             levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                             norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                         else:
                             if val_min == 0:
                                 val_min = 1e-5
                             if val_max > 1000:
                                 levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                             else:
-                                levels = np.array([00.1, 1, 2, 10, 100, val_max])
+                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
 
-                        bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
-                        norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-
                         cf = (self.axis_SNR[f].contourf(x, -y,
                                                         stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
                                                         levels, cmap='gist_rainbow',
@@ -1140,24 +1137,21 @@ class SignalProcessingTab(QWidget):
                         val_max = np.nanmax(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
                         if val_min == val_max:
                             levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                             norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                         else:
                             if val_min == 0:
                                 val_min = 1e-5
                             if val_max > 1000:
                                 levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
 
                             else:
-                                levels = np.array([00.1, 1, 2, 10, 100, val_max])
+                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
 
-                        bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
-                        norm = BoundaryNorm(boundaries=bounds, ncolors=300)
-
                         cf = (self.axis_SNR[f].contourf(x, -y,
                                                         stg.SNR_cross_section[
                                                             self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
@@ -1172,18 +1166,18 @@ class SignalProcessingTab(QWidget):
                         val_max = np.nanmax(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
                         if val_min == val_max:
                             levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                            bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                             norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                         else:
                             if val_min == 0:
                                 val_min = 1e-5
                             if val_max > 1000:
                                 levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max, val_max * 1.2]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
                             else:
-                                levels = np.array([00.1, 1, 2, 10, 100, val_max])
+                                levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max*1000 + 1])
-                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000]
+                                bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
                                 norm = BoundaryNorm(boundaries=bounds, ncolors=300)
 
                         cf = (self.axis_SNR[f].contourf(x, -y,