95 lines
3.7 KiB
Python
95 lines
3.7 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/3 SSC acoustic meas project/Graphical interface project/" \
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"Data/Acoustic_data/20180107123500.aqa"
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path_noise_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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# --- 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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self._BS_raw_data = self._data_BS.V
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self._r = self._data_BS.r
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self._freq = self._data_BS.Freq
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self._freq_text = self._data_BS.freqText
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self._time = np.array([t / self._data_BS.PingRate for t in range(self._data_BS.NumProfiles)])
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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
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self._nb_profiles_per_sec = self._data_BS.ProfileRate
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self._nb_cells = self._data_BS.NumCells
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self._cell_size = self._data_BS.cellSize
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self._pulse_length = self._data_BS.TxPulseLength
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self._nb_pings_per_sec = self._data_BS.PingRate
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self._nb_pings_averaged_per_profile = self._data_BS.Average
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self._kt = self._data_BS.Kt
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self._gain_rx = self._data_BS.RxGain
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self._gain_tx = self._data_BS.TxGain
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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(["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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# fig, ax = plt.subplots(nrows=1, ncols=1)
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# ax.pcolormesh(self._time, self._r, np.flipud(self._BS_raw_data[:, 1, :]),
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# 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.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], 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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# plt.show()
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# print(self.reshape_BS_raw_cross_section()[0, 0])
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# self.reshape_r()
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# self.reshape_t()
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# dataframe = self.concatenate_data()
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# print(dataframe)
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def reshape_BS_raw_cross_section(self):
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BS_raw_cross_section = np.reshape(self._BS_raw_data,
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(self._r.shape[0]*len(self._time), self._freq.shape[0]),
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order="F")
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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, 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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return r
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def reshape_t(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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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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