diff --git a/Model/acoustic_data_loader.py b/Model/acoustic_data_loader.py
index 423185e..5748736 100644
--- a/Model/acoustic_data_loader.py
+++ b/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)
-
diff --git a/Model/acoustic_inversion_method_high_concentration.py b/Model/acoustic_inversion_method_high_concentration.py
index 83482b3..b2a2cbd 100644
--- a/Model/acoustic_inversion_method_high_concentration.py
+++ b/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 -------------
- # ------------- 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)
+ # ------------- 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
- 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
diff --git a/README.md b/README.md
index bc36c15..1721292 100644
--- a/README.md
+++ b/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
+
+
+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
-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 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.
+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 .
+
+## 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 .