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UBSediFlow data successfully loaded

dev-brahim
brahim 2023-10-19 15:10:01 +02:00
parent 7d359983f0
commit b01b4541bb
2 changed files with 179 additions and 47 deletions
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211
Model/acoustic_data_loader_UBSediFlow.py
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@ -8,16 +8,34 @@ from matplotlib.colors import LogNorm, BoundaryNorm
from Model.udt_extract.raw_extract import raw_extract
# raw_20210519_102332.udt raw_20210520_135452.udt raw_20210525_092759.udt raw_20210525_080454.udt
path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/"
"Rhone_20210519/Rhone_20210519/record/")
filename0 = "raw_20210526_153310.udt"
# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/Raw_data_udt/")
# filename0 = "raw_20210519_135400.udt"
# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
# "APAVER_2021/transect_ubsediflow/01-raw_20210519_115128/Raw_data_udt/")
# filename0 = "raw_20210519_115128.udt"
# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
# "APAVER_2021/transect_ubsediflow/02-bb0077eda128f3f7887052eb3e8b0884/Raw_data_udt/")
# filename0 = "raw_20210519_161400.udt"
# path_BS_raw_data0 = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/"
# "APAVER_2021/transect_ubsediflow/04-fb53d0e92c9c88e2a6cf45e0320fbc76/Raw_data_udt/")
# filename0 = "raw_20210520_133200.udt"
# ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/"
# "Rhone_20210519/Rhone_20210519/record/")
# filename = "raw_20210519_115128.udt"
# "raw_20210526_153310.udt"
class AcousticDataLoaderUBSediFlow():
def __init__(self, path_BS_raw_data: str):
path_BS_raw_data = path_BS_raw_data0 + filename0
# path_BS_raw_data = path_BS_raw_data0 + filename0
self.path_BS_raw_data = path_BS_raw_data
# --- Extract Backscatter acoustic raw data with class ---
@ -49,12 +67,12 @@ class AcousticDataLoaderUBSediFlow():
time_len = []
time_snr_len = []
for config in param_us_dicts.keys():
# print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
# print(f"config : {config} \n")
print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
print(f"config : {config} \n")
for channel in param_us_dicts[config].keys():
# print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
# print(f"channel : {channel} \n")
print(data_us_dicts[config][channel].keys())
print("-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x")
print(f"channel : {channel} \n")
# print(param_us_dicts[1][1])
# print(data_us_dicts[config][channel]['echo_avg_profile'])
# --- Frequencies ---
@ -126,28 +144,80 @@ class AcousticDataLoaderUBSediFlow():
# print(f"self._time.shape {self._time.shape}")
# --- US Backscatter raw signal + SNR data ---
for f, freq in enumerate(self._freq):
if f == 0:
# print(data_us_dicts[config][channel]['echo_avg_profile']['data'])
self._BS_raw_data = np.array([np.reshape(data_us_dicts[config][channel]['echo_avg_profile']['data'],
(self._time.shape[1], self._r.shape[1])).transpose()])
# print(self._BS_raw_data.shape)
self._SNR_data = np.array(
[np.reshape(np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data']),
(self._time.shape[1], self._r.shape[1])).transpose()])
else:
BS_data = np.array([[]])
if config == 1:
BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
print("BS_data shape ", BS_data.shape)
for i in range(self._time.shape[1]):
BS_data = np.append(BS_data,
np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
axis=0)
print("BS_data shape ", BS_data.shape)
self._BS_raw_data = np.array([BS_data[:self._time.shape[1], :].transpose()])
print("BS_raw_data shape ", self._BS_raw_data.shape)
# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
# pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
# np.log(self._BS_raw_data[0, :, :]),
# cmap='Blues')
# fig.colorbar(pcm, ax=ax, shrink=1, location='right')
# plt.show()
else:
BS_data = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
print("BS_data shape ", BS_data.shape)
for i in range(self._time.shape[1]):
BS_data = np.append(BS_data,
np.array(
[data_us_dicts[config][channel]['echo_avg_profile']['data'][i]]),
axis=0)
print("BS_data shape ", BS_data.shape)
if BS_data.shape[0] > self._BS_raw_data.shape[2]:
self._BS_raw_data = np.append(self._BS_raw_data,
np.array([np.reshape(np.array(
data_us_dicts[config][channel]['echo_avg_profile']['data']),
(self._time.shape[1], self._r.shape[1])).transpose()]),
np.array([BS_data[:self._BS_raw_data.shape[2], :].transpose()]),
axis=0)
self._SNR_data = np.append(self._SNR_data,
np.array([np.reshape(np.array(
np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data'])),
(self._time.shape[1], self._r.shape[1])).transpose()]),
elif BS_data.shape[0] < self._BS_raw_data.shape[2]:
self._BS_raw_data = np.append(self._BS_raw_data[config-1, :, BS_data.shape[0]],
np.array([BS_data.transpose()]),
axis=0)
# print(self._BS_raw_data.shape)
else:
self._BS_raw_data = np.append(self._BS_raw_data, np.array([BS_data.transpose()]), axis=0)
print("BS_raw_data shape ", self._BS_raw_data.shape)
# if f == 0:
# print(np.array(data_us_dicts[config][channel]['echo_avg_profile']['data'][0]).shape)
# self._BS_raw_data[f, :, :] = np.array([data_us_dicts[config][channel]['echo_avg_profile']['data'][0]])
# # self._BS_raw_data = np.array([np.reshape(data_us_dicts[config][channel]['echo_avg_profile']['data'],
# # (self._time.shape[1], self._r.shape[1])).transpose()])
# print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
# self._SNR_data = np.array(
# [np.reshape(np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data']),
# (self._time.shape[1], self._r.shape[1])).transpose()])
# else:
# # self._BS_raw_data = np.append(self._BS_raw_data,
# # np.array(data_us_dicts[config][channel]['echo_avg_profile']['data']),
# # (self._r.shape[1], self._time.shape[1]))]),
# # axis=0)
# # self._BS_raw_data = np.append(self._BS_raw_data,
# # np.array([np.reshape(np.array(
# # data_us_dicts[config][channel]['echo_avg_profile']['data']),
# # (self._time.shape[1], self._r.shape[1])).transpose()]),
# # axis=0)
#
# self._SNR_data = np.append(self._SNR_data,
# np.array([np.reshape(np.array(
# np.abs(data_us_dicts[config][channel]['snr_doppler_avg_profile']['data'])),
# (self._time.shape[1], self._r.shape[1])).transpose()]),
# axis=0)
# # print(self._BS_raw_data.shape)
# --- US Backscatter raw signal ---
@ -155,6 +225,15 @@ class AcousticDataLoaderUBSediFlow():
# print(len(self._BS_raw_data))
# print(self._BS_raw_data)
if self._time.shape[1] > self._BS_raw_data.shape[2]:
self._time = self._time[:, :self._BS_raw_data.shape[2]]
elif self._time.shape[1] < self._BS_raw_data.shape[2]:
self._BS_raw_data = self._BS_raw_data[:, :, :self._time.shape[1]]
else:
self._time = self._time
self._BS_raw_data = self._BS_raw_data
self._time = self._time[:, :self._BS_raw_data.shape[2]]
print("self._time.shape ", self._time.shape)
print("self._r.shape ", self._r.shape)
@ -166,8 +245,8 @@ class AcousticDataLoaderUBSediFlow():
# self._BS_raw_data = np.array(np.reshape(self._BS_raw_data, (len(self._freq), self._r.shape[1], self._time.shape[1])))
print("self._BS_raw_data.shape ", self._BS_raw_data.shape)
print("self._SNR_data.shape ", self._SNR_data.shape)
print(self._SNR_data)
# print("self._SNR_data.shape ", self._SNR_data.shape)
# print(self._SNR_data)
# print("device_name ", device_name, "\n")
@ -217,12 +296,30 @@ class AcousticDataLoaderUBSediFlow():
# print(np.where((self._time) == 155)[0][0])
# --- Plot Backscatter US data ---
# fig, ax = plt.subplots(nrows=len(self._freq), ncols=1)
# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
# # pcm = ax.pcolormesh(self._time[0, :], self._r[0, :], np.log(self._BS_raw_data[0, :, :]),
# # cmap='Blues')#, shading='gouraud')
# pcm = ax.pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
# np.log(self._BS_raw_data[0, :, :]),
# cmap='Blues') # , shading='gouraud')
# # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
# fig.colorbar(pcm, ax=ax, shrink=1, location='right')
# plt.show()
# fig, ax = plt.subplots(nrows=len(self._freq), ncols=1, layout="constrained")
# for f, freq in enumerate(self._freq):
# # print(f"{f} : {freq} \n")
# pcm = ax[f].pcolormesh(self._time[f, :], self._r[f, :], (self._BS_raw_data[f, :, :self._time.shape[1]]),
# cmap='viridis',
# norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
# print(f"{f} : {freq} \n")
# # pcm = ax[f].imshow(np.log(self._BS_raw_data[f, :, :self._time.shape[1]]),
# # cmap='Blues')
# # pcm = ax[f].pcolormesh(list(range(self._BS_raw_data.shape[2])), list(range(self._BS_raw_data.shape[1])),
# # np.log(self._BS_raw_data[f, :, :]),
# # cmap='Blues', shading='gouraud')
# pcm = ax[f].pcolormesh(self._time[f, :], self._r[f, :], np.log(self._BS_raw_data[f, :, :]),
# cmap='viridis', shading='gouraud')
# # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
# fig.colorbar(pcm, ax=ax[:], shrink=1, location='right')
@ -277,6 +374,36 @@ class AcousticDataLoaderUBSediFlow():
# self.reshape_r()
# self.reshape_t()
# Lecture du fichier excel
# path = ("/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/Data/APAVER_2021/"
# "transect_ubsediflow/01-raw_20210519_115128/Raw_data_csv/config_1/"
# "echo_avg_profile_1_1_20210519_115128.csv")
#
# df = pd.read_csv(path, sep="\t")
#
# arr = []
# for column in df.columns:
# arr.append(df[column].to_numpy())
# # arr = np.append(arr, np.array([df[column].to_numpy()]), axis=0)
# arr = arr[1:]
# print(len(arr))
#
# matrix = np.array([arr[0]])
# print(matrix.shape)
# for i in range(len(arr)-1):
# matrix = np.append(matrix, np.array([arr[i]]), axis=0)
# print(matrix.shape)
# fig, ax = plt.subplots(nrows=1, ncols=1, layout="constrained")
# pcm = ax.pcolormesh(list(range(matrix.shape[1])), list(range(matrix.shape[0])), np.log(matrix),
# cmap='Blues')#, shading='gouraud')
# # norm=LogNorm(vmin=np.min(self._BS_raw_data[f, :, :]), vmax=np.max(self._BS_raw_data[f, :, :])), shading='gouraud')
# # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
# # norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :]))) # , shading='gouraud')
# fig.colorbar(pcm, ax=ax, shrink=1, location='right')
# plt.show()
def reshape_BS_raw_cross_section(self):
BS_raw_cross_section = np.reshape(self._BS_raw_data,
(self._r.shape[1]*self._time.shape[1], len(self._freq)),
@ -284,12 +411,12 @@ class AcousticDataLoaderUBSediFlow():
# print(BS_raw_cross_section.shape)
return BS_raw_cross_section
def reshape_SNR_data(self):
SNR_data = np.reshape(self._SNR_data,
(self._r.shape[1]*self._time.shape[1], len(self._freq)),
order="F")
# print(BS_raw_cross_section.shape)
return SNR_data
# def reshape_SNR_data(self):
# SNR_data = np.reshape(self._SNR_data,
# (self._r.shape[1]*self._time.shape[1], len(self._freq)),
# order="F")
# # print(BS_raw_cross_section.shape)
# return SNR_data
def reshape_r(self):
r = np.zeros((self._r.shape[1]*self._time.shape[1], len(self._freq)))
@ -326,7 +453,7 @@ class AcousticDataLoaderUBSediFlow():
# return df
if __name__ == "__main__":
AcousticDataLoaderUBSediFlow(path_BS_raw_data0 + filename0)
# if __name__ == "__main__":
# AcousticDataLoaderUBSediFlow(path_BS_raw_data0 + filename0)

15
View/acoustic_data_tab.py
View File

@ -703,18 +703,23 @@ class AcousticDataTab(QWidget):
filename = QFileDialog.getOpenFileName(self, "Open file", "", "UBSediFlow file (*.udt)")
dir_name = path.dirname(filename[0])
name = path.basename(filename[0])
print(dir_name, name)
# --- Fill lineEdit with path and file names + load acoustic data ---
# --- fill date, hour and measurements information + fill frequency combobox for bottom detection ---
if self.combobox_ABS_system_choice.currentIndex() != 0:
if self.sender().objectName() == "pushbutton_acoustic_file":
# stg.path_BS_raw_data = dir_name
# stg.filename_BS_raw_data = name
# self.load_BS_acoustic_raw_data()
try:
stg.path_BS_raw_data = dir_name
stg.filename_BS_raw_data = name
self.load_BS_acoustic_raw_data()
print("0 Je suis sur la donnée BS")
except ValueError as e:
print("error : ", e)
print("1 Je suis sur la donnée BS")
msgBox = QMessageBox()
msgBox.setWindowTitle("Download Error")
@ -793,7 +798,7 @@ class AcousticDataTab(QWidget):
stg.r_reshape = acoustic_data.reshape_r()
stg.time_reshape = acoustic_data.reshape_t()
stg.snr = acoustic_data._SNR_data
stg.snr_reshape = acoustic_data.reshape_SNR_data()
# stg.snr_reshape = acoustic_data.reshape_SNR_data()
def load_noise_data_and_compute_SNR(self):
if self.combobox_ABS_system_choice.currentIndex() == 1:
@ -990,8 +995,8 @@ class AcousticDataTab(QWidget):
pcm = self.axis_BS[f].pcolormesh(
stg.time[f, int(stg.tmin[f]):int(stg.tmax[f])], -stg.r[f, :],
stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
np.log(stg.BS_raw_data[f, :, int(stg.tmin[f]):int(stg.tmax[f])]),
cmap='Blues')#, norm=LogNorm(vmin=val_min, vmax=val_max))
self.axis_BS[f].text(1, .70, stg.freq_text[f],
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
@ -1065,8 +1070,8 @@ class AcousticDataTab(QWidget):
# print("stg.t[f, :].shape ", stg.t[f])
# print("stg.r[f, :].shape ", stg.r[f, :])
pcm = self.axis_BS[f].pcolormesh(stg.t[f, :], -stg.r[f, :], stg.BS_data[f, :, :],
cmap='viridis', norm=LogNorm(vmin=val_min, vmax=val_max))
pcm = self.axis_BS[f].pcolormesh(stg.t[f, :], -stg.r[f, :], np.log(stg.BS_data[f, :, :]),
cmap='Blues')#, norm=LogNorm(vmin=val_min, vmax=val_max))
self.axis_BS[f].text(1, .70, stg.freq_text[f],
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,