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