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acoused
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acoustic data loader file is cleaned from useless commented lines and useless print

dev-brahim
brahim 2025-03-06 10:18:46 +01:00
parent 9ecb70b955
commit cea9e35498
1 changed files with 0 additions and 77 deletions
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Model/acoustic_data_loader.py
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@ -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)