Loader for the data of UBSediFlow is adapted when length in depth (vertical) and time (horizontal) is not the same for each frequencies.
brahim
parent
343fc27aec
commit
bfdbd74720
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@ -174,109 +174,214 @@ class AcousticDataLoaderUBSediFlow:
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# self._time_snr = np.append(self._time_snr, time_snr, axis=0)
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# # print(f"self._time.shape {self._time.shape}")
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# --- US Backscatter raw signal + SNR data ---
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# --- US Backscatter raw signal ---
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BS_data = np.array([[]])
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if config == 1:
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BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# print("BS_data shape ", BS_data.shape)
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# print("******************************")
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# date_list = [np.abs(datetime.datetime(2021, 5, 19, 14, 10, 00).timestamp()
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# - date.timestamp()) for date in data_us_dicts[config][channel]['echo_avg_profile']['time']]
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# print(date_list)
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# print(np.where(date_list == np.min(date_list)))
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# print((data_us_dicts[config][channel]['echo_avg_profile']['time'][np.where(date_list == np.min(date_list))[0][0]] -
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# data_us_dicts[config][channel]['echo_avg_profile']['time'][0]).total_seconds())
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# # == datetime.datetime(2021, 5, 19, 14, 10, 2, 644000))
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# print("******************************")
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for i in range(self._time.shape[1]):
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BS_data = np.append(BS_data,
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np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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axis=0)
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print("0. BS_data shape ", BS_data.shape)
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self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
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print("0. BS_raw_data shape ", self._BS_raw_data.shape)
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# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
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# pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
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# np.log(self._BS_raw_data[0, :, :]),
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# cmap='Blues')
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# fig.colorbar(pcm, ax=ax, shrink=1, location='right')
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# plt.show()
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else:
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BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# print("BS_data shape ", BS_data.shape)
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print(f"cas 1 : BS_raw_data shape = {self._BS_raw_data.shape}")
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print(f"cas 1 : BS_data shape = {BS_data.shape}")
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for i in range(self._time.shape[1]):
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BS_data = np.append(BS_data,
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np.array(
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[data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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axis=0)
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print("1. BS_data shape ", BS_data.shape)
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#-----------------------------------------------------------------------------------------------------------------------
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# Ici il faut écrire les conditions sur les tailles selon r et selon time
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# donc sur BS_data.shape[0] (time) et BS_data.shape[1] (depth)
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#-----------------------------------------------------------------------------------------------------------------------
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self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
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# print(f"a) BS_data shape = {BS_data.shape}")
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# print(f"a) BS_raw_data shape = {BS_raw_data.shape}")
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else:
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BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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print(f"{config}) BS_data shape = {BS_data.shape}")
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for j in range(self._time.shape[1]):
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BS_data = np.append(BS_data,
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np.array(
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[data_us_dicts[config][channel]['echo_avg_profile']['data'][j]]),
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axis=0)
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BS_data = np.array([BS_data.transpose()])
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print(f"xxxx BS_data shape = {BS_data.shape}")
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# print(f"b) BS_raw_data shape = {BS_raw_data.shape}")
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# 1- time shape > BS data shape
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# <=> data recorded with the frequency are longer than data recorded with the other lower frequencies
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if (BS_data.shape[0] > self._BS_raw_data.shape[2]):
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self._BS_raw_data = np.append(self._BS_raw_data,
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np.array([BS_data[:self._BS_raw_data.shape[2], :].transpose()]),
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axis=0)
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if (BS_data.shape[2] > self._BS_raw_data.shape[2]):
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print(f"cas 2 : BS_raw_data shape = {self._BS_raw_data.shape}")
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print(f"cas 2 : BS_data shape = {BS_data.shape}")
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if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
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# print(f"BS_data shape[0] = {BS_data.shape[0]}")
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# print(f"BS_raw_data shape[2] = {BS_raw_data.shape[2]}")
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self._BS_raw_data = np.append(self._BS_raw_data,
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BS_data[:, :self._BS_raw_data.shape[1], :self._BS_raw_data.shape[2]],
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axis=0)
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elif (BS_data.shape[1] < self._BS_raw_data.shape[1]):
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self._BS_raw_data = np.append(self._BS_raw_data[:, :BS_data.shape[1], :],
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BS_data[:, :, :self._BS_raw_data.shape[2]],
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axis=0)
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else:
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self._BS_raw_data = np.append(self._BS_raw_data,
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BS_data[:, :, :self._BS_raw_data.shape[2]],
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axis=0)
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# 2- time shape < BS data shape
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# <=> data recorded with the frequency are shorter than data recorded with the other lower frequencies
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elif BS_data.shape[0] < self._BS_raw_data.shape[2]:
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self._BS_raw_data = np.append(self._BS_raw_data[config-1, :, BS_data.shape[0]],
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np.array([BS_data.transpose()]),
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axis=0)
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elif BS_data.shape[2] < self._BS_raw_data.shape[2]:
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print(f"cas 3 : BS_raw_data shape = {self._BS_raw_data.shape}")
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print(f"cas 3 : BS_data shape = {BS_data.shape}")
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if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
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self._BS_raw_data = np.append(self._BS_raw_data[:, :, BS_data.shape[2]],
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BS_data[:, :self._BS_raw_data.shape[1], :],
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axis=0)
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elif (BS_data.shape[1] < self._BS_raw_data.shape[1]):
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self._BS_raw_data = np.append(self._BS_raw_data[:, :BS_data.shape[1], BS_data.shape[2]],
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BS_data,
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axis=0)
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else:
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self._BS_raw_data = np.append(self._BS_raw_data[:, :, BS_data.shape[0]],
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BS_data,
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axis=0)
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# print(f"d) BS_data shape = {BS_data.shape}")
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# print(f"d) BS_raw_data shape = {BS_raw_data.shape}")
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# 3- time shape = BS data shape
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# <=> data recorded with the frequency have the same duration than data recorded with the other lower frequency
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else:
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self._BS_raw_data = np.append(self._BS_raw_data, np.array([BS_data.transpose()]), axis=0)
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print(f"cas 4 : BS_raw_data shape = {self._BS_raw_data.shape}")
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print(f"cas 4 : BS_data shape = {BS_data.shape}")
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print("1. BS_raw_data shape ", self._BS_raw_data.shape)
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if (BS_data.shape[1] > self._BS_raw_data.shape[1]):
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# if f == 0:
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# print(np.array(data_us_dicts[config][channel]['echo_avg_profile']['data'][0]).shape)
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# self._BS_raw_data[f, :, :] = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# # self._BS_raw_data = np.array([np.reshape(data_us_dicts[config][channel]['echo_avg_profile']['data'],
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# # (self._time.shape[1], self._r.shape[1])).transpose()])
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# print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
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# self._SNR_data = np.array(
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# [np.reshape(np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data']),
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# (self._time.shape[1], self._r.shape[1])).transpose()])
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# else:
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# # self._BS_raw_data = np.append(self._BS_raw_data,
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# # np.array(data_us_dicts[config][channel]['echo_avg_profile']['data']),
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# # (self._r.shape[1], self._time.shape[1]))]),
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# # axis=0)
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# # self._BS_raw_data = np.append(self._BS_raw_data,
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# # np.array([np.reshape(np.array(
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# # data_us_dicts[config][channel]['echo_avg_profile']['data']),
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# # (self._time.shape[1], self._r.shape[1])).transpose()]),
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# # axis=0)
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#
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# self._SNR_data = np.append(self._SNR_data,
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# np.array([np.reshape(np.array(
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# np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data'])),
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# (self._time.shape[1], self._r.shape[1])).transpose()]),
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# axis=0)
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# # print(self._BS_raw_data.shape)
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self._BS_raw_data = np.append(self._BS_raw_data,
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BS_data[:, :self._BS_raw_data.shape[1], :], axis=0)
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# --- US Backscatter raw signal ---
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elif (BS_data.shape[1] < self._BS_raw_data.shape[1]):
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self._BS_raw_data = np.append(self._BS_raw_data[:, :BS_data.shape[1], :],
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BS_data, axis=0)
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# print(len(self._BS_raw_data))
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# print(self._BS_raw_data)
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else:
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self._BS_raw_data = np.append(self._BS_raw_data,
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BS_data, axis=0)
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# print(f"e) BS_data shape = {BS_data.shape}")
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print("Final BS_raw_data shape = ", self._BS_raw_data.shape)
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print("********************************************")
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# # --- US Backscatter raw signal + SNR data ---
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# BS_data = np.array([[]])
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#
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# if config == 1:
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# BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# # print("BS_data shape ", BS_data.shape)
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# # print("******************************")
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# # date_list = [np.abs(datetime.datetime(2021, 5, 19, 14, 10, 00).timestamp()
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# # - date.timestamp()) for date in data_us_dicts[config][channel]['echo_avg_profile']['time']]
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# # print(date_list)
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# # print(np.where(date_list == np.min(date_list)))
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# # print((data_us_dicts[config][channel]['echo_avg_profile']['time'][np.where(date_list == np.min(date_list))[0][0]] -
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# # data_us_dicts[config][channel]['echo_avg_profile']['time'][0]).total_seconds())
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# # # == datetime.datetime(2021, 5, 19, 14, 10, 2, 644000))
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# # print("******************************")
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#
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# for i in range(self._time.shape[1]):
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# BS_data = np.append(BS_data,
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# np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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# axis=0)
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# print("0. BS_data shape ", BS_data.shape)
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#
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# self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
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#
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# print("0. BS_raw_data shape ", self._BS_raw_data.shape)
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#
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# # fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
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# # pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
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# # np.log(self._BS_raw_data[0, :, :]),
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# # cmap='Blues')
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# # fig.colorbar(pcm, ax=ax, shrink=1, location='right')
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# # plt.show()
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#
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# else:
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#
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# BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# # print("BS_data shape ", BS_data.shape)
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# for i in range(self._time.shape[1]):
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# BS_data = np.append(BS_data,
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# np.array(
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# [data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
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# axis=0)
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# print("1. BS_data shape ", BS_data.shape)
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#
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# #-----------------------------------------------------------------------------------------------------------------------
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# # Ici il faut écrire les conditions sur les tailles selon r et selon time
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# # donc sur BS_data.shape[0] (time) et BS_data.shape[1] (depth)
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# #-----------------------------------------------------------------------------------------------------------------------
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#
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# # 1- time shape > BS data shape
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# # <=> data recorded with the frequency are longer than data recorded with the other lower frequencies
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# if (BS_data.shape[0] > self._BS_raw_data.shape[2]):
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# self._BS_raw_data = np.append(self._BS_raw_data,
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# np.array([BS_data[:self._BS_raw_data.shape[2], :].transpose()]),
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# axis=0)
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#
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# # 2- time shape < BS data shape
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# # <=> data recorded with the frequency are shorter than data recorded with the other lower frequencies
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# elif BS_data.shape[0] < self._BS_raw_data.shape[2]:
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# self._BS_raw_data = np.append(self._BS_raw_data[config-1, :, BS_data.shape[0]],
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# np.array([BS_data.transpose()]),
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# axis=0)
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#
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# # 3- time shape = BS data shape
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# # <=> data recorded with the frequency have the same duration than data recorded with the other lower frequency
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# else:
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# self._BS_raw_data = np.append(self._BS_raw_data, np.array([BS_data.transpose()]), axis=0)
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#
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#
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# print("1. BS_raw_data shape ", self._BS_raw_data.shape)
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#
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# # if f == 0:
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# # print(np.array(data_us_dicts[config][channel]['echo_avg_profile']['data'][0]).shape)
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# # self._BS_raw_data[f, :, :] = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
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# # # self._BS_raw_data = np.array([np.reshape(data_us_dicts[config][channel]['echo_avg_profile']['data'],
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# # # (self._time.shape[1], self._r.shape[1])).transpose()])
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# # print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
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# # self._SNR_data = np.array(
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# # [np.reshape(np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data']),
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# # (self._time.shape[1], self._r.shape[1])).transpose()])
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# # else:
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# # # self._BS_raw_data = np.append(self._BS_raw_data,
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# # # np.array(data_us_dicts[config][channel]['echo_avg_profile']['data']),
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# # # (self._r.shape[1], self._time.shape[1]))]),
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# # # axis=0)
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# # # self._BS_raw_data = np.append(self._BS_raw_data,
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# # # np.array([np.reshape(np.array(
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# # # data_us_dicts[config][channel]['echo_avg_profile']['data']),
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# # # (self._time.shape[1], self._r.shape[1])).transpose()]),
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# # # axis=0)
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# #
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# # self._SNR_data = np.append(self._SNR_data,
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# # np.array([np.reshape(np.array(
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# # np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data'])),
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# # (self._time.shape[1], self._r.shape[1])).transpose()]),
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# # axis=0)
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# # # print(self._BS_raw_data.shape)
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#
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# # --- US Backscatter raw signal ---
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#
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#
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# # print(len(self._BS_raw_data))
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# # print(self._BS_raw_data)
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if self._time.shape[1] > self._BS_raw_data.shape[2]:
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self._time = self._time[:, :self._BS_raw_data.shape[2]]
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