from Model.AquascatDataLoader import RawAquascatData import numpy as np 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() 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(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[0, :])[:, 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)