Measurements Information group box has been standardized between Aquascat and UBSediflow: the same characteristics are used and value are displayed according to the frequency. Note that the modifications have been made for delete and clear data (but not yet finished).

Model/acoustic_data_loader.py

@@ -5,8 +5,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from matplotlib.colors import LogNorm
 
-# path_BS_raw_data = "/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/" \
-#                         "Data/Acoustic_data/20180107123500.aqa"
+# 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"
 
@@ -16,11 +15,11 @@ 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}")
 
@@ -38,19 +37,19 @@ class AcousticDataLoader:
 
         self._date = self._data_BS.date.date()
         self._hour = self._data_BS.date.time()
-        self._nb_profiles = self._data_BS.NumProfiles
-        self._nb_profiles_per_sec = self._data_BS.ProfileRate
-        self._nb_cells = self._data_BS.NumCells
-        self._cell_size = self._data_BS.cellSize
-        self._pulse_length = self._data_BS.TxPulseLength
-        self._nb_pings_per_sec = self._data_BS.PingRate
-        self._nb_pings_averaged_per_profile = self._data_BS.Average
-        self._kt = self._data_BS.Kt
-        self._gain_rx = self._data_BS.RxGain
-        self._gain_tx = self._data_BS.TxGain
+        self._nb_profiles = [self._data_BS.NumProfiles]*self._freq.shape[0]
+        self._nb_profiles_per_sec = [self._data_BS.ProfileRate]*self._freq.shape[0]
+        self._nb_cells = [self._data_BS.NumCells]*self._freq.shape[0]
+        self._cell_size = [self._data_BS.cellSize]*self._freq.shape[0]
+        self._pulse_length = [self._data_BS.TxPulseLength]*self._freq.shape[0]
+        self._nb_pings_per_sec = [self._data_BS.PingRate]*self._freq.shape[0]
+        self._nb_pings_averaged_per_profile = [self._data_BS.Average]*self._freq.shape[0]
+        self._kt = self._data_BS.Kt.tolist()
+        self._gain_rx = self._data_BS.RxGain.tolist()
+        self._gain_tx = self._data_BS.TxGain.tolist()
 
-        print(self._r[0, :][1] - self._r[1, :][0])
-        print(self._kt)
+        # 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([])

Model/acoustic_data_loader_UBSediFlow.py

@@ -32,7 +32,7 @@ from Model.udt_extract.raw_extract import raw_extract
 # path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/Raw_data_udt/")
 # filename0 = "raw_20210519_130643.udt"
 
-# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/Raw_data_udt/")
+# path_BS_raw_data0 = ("/home/bmoudjed/Documents/2 Data/APAVER_2021/Raw_data_udt/")
 # filename0 = "raw_20210520_085958.udt"
 
 # filename = "raw_20210519_115128.udt"
@@ -54,23 +54,36 @@ class AcousticDataLoaderUBSediFlow:
             = raw_extract(self.path_BS_raw_data)
 
         print(f"device_name : {device_name}")
-        print(f"time_begin : {time_begin}")
+        print(f"date begin : {time_begin.date()}")
+        print(f"time begin : {time_begin.time()}")
         print(f"settings_dict : {settings_dict}")
 
-        # --- Date and Hour of measurements read on udt data file ---
-        filename = self.path_BS_raw_data[-23:]
-        date_and_time = datetime.datetime(year=int(filename[4:8]),
-                                          month=int(filename[8:10]),
-                                          day=int(filename[10:12]),
-                                          hour=int(filename[13:15]),
-                                          minute=int(filename[15:17]),
-                                          second=int(filename[17:19]))
-        self._date = date_and_time.date()
+        # # --- Date and Hour of measurements read on udt data file ---
+        # filename = self.path_BS_raw_data[-23:]
+        # date_and_time = datetime.datetime(year=int(filename[4:8]),
+        #                                   month=int(filename[8:10]),
+        #                                   day=int(filename[10:12]),
+        #                                   hour=int(filename[13:15]),
+        #                                   minute=int(filename[15:17]),
+        #                                   second=int(filename[17:19]))
+        self._date = time_begin.date()
         print(f"date : {self._date}")
-        self._hour = date_and_time.time()
+        self._hour = time_begin.time()
         print(f"time : {self._hour}")
 
         self._freq = np.array([[]])
+
+        self._nb_profiles = []
+        self._nb_profiles_per_sec = []
+        self._nb_cells = []
+        self._cell_size = []
+        self._pulse_length = []
+        self._nb_pings_per_sec = []
+        self._nb_pings_averaged_per_profile = []
+        self._kt = []
+        self._gain_rx = []
+        self._gain_tx = []
+
         self._r = np.array([[]])
         self._time = np.array([[]])
         self._time_snr = np.array([[]])
@@ -78,18 +91,29 @@ class AcousticDataLoaderUBSediFlow:
         # self._SNR_data = np.array([[[]]])
         time_len = []
         time_snr_len = []
+
         for config in param_us_dicts.keys():
             # print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
             # print(f"config : {config} \n")
             for channel in param_us_dicts[config].keys():
                 print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
                 # print(f"channel : {channel} \n")
-                print(param_us_dicts[config][channel])
+                # print("param_us_dicts[config][channel] ", param_us_dicts[config][channel])
+                # print("param_us_dicts ", param_us_dicts)
                 # print(data_us_dicts[config][channel]['echo_avg_profile'])
 
                 # --- Frequencies ---
                 self._freq = np.append(self._freq, param_us_dicts[config][channel]['f0'])
 
+                self._nb_cells = np.append(self._nb_cells, param_us_dicts[config][channel]['n_cell'])
+                self._cell_size = np.append(self._cell_size, param_us_dicts[config][channel]['r_dcell'])
+                self._pulse_length = np.append(self._pulse_length, 0)
+                self._nb_pings_per_sec = np.append(self._nb_pings_per_sec, param_us_dicts[config][channel]['prf'])
+                self._nb_pings_averaged_per_profile = np.append(self._nb_pings_averaged_per_profile, param_us_dicts[config][channel]['n_p'])
+                self._kt = np.append(self._kt, 0)
+                self._gain_rx = np.append(self._gain_rx, param_us_dicts[config][channel]['a0'])
+                self._gain_tx = np.append(self._gain_tx, param_us_dicts[config][channel]['a1'])
+
                 # --- Depth for each frequencies ---
                 print("r_dcell : ", param_us_dicts[config][channel]['r_dcell'])
                 print("n_cell : ", param_us_dicts[config][channel]['n_cell'])
@@ -145,6 +169,10 @@ class AcousticDataLoaderUBSediFlow:
                     self._time = np.append(self._time, time, axis=0)
                     print(f"self._time.shape {self._time.shape}")
 
+                self._nb_profiles = np.append(self._nb_profiles, self._time.shape[1])
+                self._nb_profiles_per_sec = np.append(self._nb_profiles_per_sec,
+                                                      param_us_dicts[config][channel]['n_avg'])
+
                 # --- SNR Time for each frequencies ---
                 # time_snr = [[(t - data_us_dicts[config][channel]['snr_doppler_avg_profile']['time'][0]).total_seconds()
                 #          for t in data_us_dicts[config][channel]['snr_doppler_avg_profile']['time']]]
@@ -600,7 +628,7 @@ class AcousticDataLoaderUBSediFlow:
         # fig.colorbar(pcm, ax=ax, shrink=1, location='right')
         # plt.show()
 
-    def reshape_BS_raw_cross_section(self):
+    def reshape_BS_raw_data(self):
         BS_raw_cross_section = np.reshape(self._BS_raw_data,
                                           (self._r.shape[1]*self._time.shape[1], len(self._freq)),
                                           order="F")

settings.py

@@ -61,13 +61,14 @@ BS_stream_bed = []      # BS_data_section = BS data in the section. Values NaN o
 BS_noise_cross_section = np.array([])         # BS_noise_cros_section = BS_noise_data[:, :, tmin:tmax]  (former Noise_data)
 SNR_cross_section = np.array([])             # SNR_data = snr[:, :, tmin:tmax]
 SNR_stream_bed = np.array([])
-t_cross_section = []
+time_cross_section = []
 t_snr = np.array([])
 depth_cross_section = []
 depth_bottom = []
 val_bottom = []
 ind_bottom = []
 freq_bottom_detection = []
+depth_bottom_detection_1st_int_area = []
 
 # --- Processed data in Signal Processing Tab ---
 # BS_cross_section_SNR_filter = np.array([[[]]])      # BS data filtered with SNR values (remove point if SNR < value) - bottom is not detected
@@ -164,6 +165,8 @@ filename_save_as = ""
 dirname_open = ""
 filename_open = ""
 
+read_table_trigger = 0
+