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)
-