parouby
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acoused
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Merge branch 'dev-parouby' into dev

dev-brahim
Pierre-Antoine 2025-03-25 16:44:40 +01:00
parent a8bb150bf8 07353ccc3c
commit d7ea9cc4f7
11 changed files with 867 additions and 642 deletions
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4
Model/create_table_for_save_as.py
View File

@ -80,8 +80,8 @@ class CreateTableForSaveAs:
time BLOB, depth BLOB, BS_raw_data BLOB,
time_reshape BLOB, depth_reshape BLOB, BS_raw_data_reshape BLOB,
time_cross_section BLOB, depth_cross_section BLOB,
BS_cross_section BLOB, BS_stream_bed BLO B,
depth_bottom, val_bottom, ind_bottom,
BS_cross_section BLOB, BS_stream_bed BLOB,
depth_bottom BLOB, val_bottom BLOB, ind_bottom BLOB,
time_noise BLOB, depth_noise BLOB, BS_noise_raw_data BLOB,
SNR_raw_data BLOB, SNR_cross_section BLOB, SNR_stream_bed BLOB,
BS_raw_data_pre_process_SNR BLOB,

773
Model/read_table_for_open.py
View File

@ -60,384 +60,535 @@ class ReadTableForOpen:
except OSError as e:
logger.warning(f"chdir: {str(e)}")
self.sql_file_to_open = open(stg.filename_open)
self.read_table()
self.opened = True
def read_table(self):
def execute(self, query):
return self._cur.execute(query).fetchall()
def read_table(self):
stg.read_table_trigger = 1
# connexion to File db
logger.debug(f"Open '{stg.filename_open}'")
cnx = sqlite3.connect(stg.filename_open)
self._cur = cnx.cursor()
# Create database cursor to execute SQL statements and fetch results from SQL queries.
cur = cnx.cursor()
self.read_table_acoustic_file()
self.read_table_measure()
self.read_table_BS_raw_data()
self.read_table_settings()
self.read_table_sediment_file()
self.read_table_table_sediment_data()
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++++++++
# --- Table Acoustic File ---
# +++++++++++++++++++++++++++
logger.debug(f"Reading '{stg.filename_open}' done")
self._cur.close()
cnx.close()
logger.debug(f"'{stg.filename_open}' closed")
def read_table_acoustic_file(self):
query0 = f'''SELECT acoustic_data FROM AcousticFile'''
data0 = cur.execute(query0).fetchall()
print("data0 ", data0)
data0 = self.execute(query0)
logger.debug(f"data0: {data0}")
stg.acoustic_data = [x[0] for x in data0]
print("stg.acoustic_data ", stg.acoustic_data)
logger.debug(f"stg.acoustic_data: {stg.acoustic_data}")
for k in range(len(stg.acoustic_data)):
print("hello")
query = f'''SELECT acoustic_data, acoustic_file, ABS_name, path_BS_noise_data, filename_BS_noise_data,
noise_method, noise_value, data_preprocessed FROM AcousticFile WHERE (acoustic_data = {k})'''
data = cur.execute(query).fetchall()
query = f'''
SELECT
acoustic_data, acoustic_file, ABS_name, path_BS_noise_data,
filename_BS_noise_data,
noise_method, noise_value, data_preprocessed
FROM AcousticFile
WHERE (acoustic_data = {k})
'''
data = self.execute(query)[0]
print("data acoustic file", data)
stg.filename_BS_raw_data.append([str(y[1]) + '.aqa' for y in data][0])
stg.ABS_name.append([z[2] for z in data][0])
stg.path_BS_noise_data.append([z[3] for z in data][0])
stg.filename_BS_noise_data.append([z[4] for z in data][0])
stg.noise_method.append([z[5] for z in data][0])
stg.noise_value.append([z[6] for z in data][0])
stg.data_preprocessed.append([z[7] for z in data][0])
stg.filename_BS_raw_data.append(
str(data[1]) + '.aqa'
)
stg.ABS_name.append(data[2])
stg.path_BS_noise_data.append(data[3])
stg.filename_BS_noise_data.append(data[4])
stg.noise_method.append(data[5])
stg.noise_value.append(data[6])
stg.data_preprocessed.append(data[7])
print("data acoustic file ", stg.filename_BS_raw_data, stg.ABS_name, stg.path_BS_noise_data, stg.filename_BS_noise_data,
stg.noise_method, stg.noise_value, stg.data_preprocessed)
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++
# --- Table Measure ---
# +++++++++++++++++++++
logger.debug("data acoustic file:")
logger.debug(f"- {stg.filename_BS_raw_data}")
logger.debug(f"- {stg.ABS_name}")
logger.debug(f"- {stg.path_BS_noise_data}")
logger.debug(f"- {stg.filename_BS_noise_data}")
logger.debug(f"- {stg.noise_method}")
logger.debug(f"- {stg.noise_value}")
logger.debug(f"- {stg.data_preprocessed}")
def read_table_measure(self):
stg.date = [0]*len(stg.acoustic_data)
stg.hour = [0]*len(stg.acoustic_data)
for i in range(len(stg.acoustic_data)):
print("i = ", i)
query1 = f'''SELECT acoustic_data, Date, Hour, frequency, sound_attenuation, kt_read, kt_corrected, NbProfiles,
NbProfilesPerSeconds, NbCells, CellSize, PulseLength, NbPingsPerSeconds, NbPingsAveragedPerProfile,
GainRx, GainTx FROM Measure WHERE (acoustic_data = {i})'''
data1 = cur.execute(query1).fetchall()
print("--------------------------------------")
print("data1 ", data1)
for i in range(len(stg.acoustic_data)):
query1 = f'''
SELECT
acoustic_data, Date, Hour, frequency,
sound_attenuation, kt_read, kt_corrected, NbProfiles,
NbProfilesPerSeconds, NbCells, CellSize, PulseLength,
NbPingsPerSeconds, NbPingsAveragedPerProfile,
GainRx, GainTx
FROM Measure
WHERE (acoustic_data = {i})
'''
data1 = self.execute(query1)
logger.debug(f"data1 for {i}: {data1}")
stg.date[i] = data1[0][1]
stg.hour[i] = data1[0][2]
stg.freq.append(np.array([x[3] for x in data1]))
stg.freq_text.append([str(x[3]*1e-6) + 'MHz' for x in data1])
stg.water_attenuation.append([x[4] for x in data1])
stg.kt_read.append([x[5] for x in data1])
stg.freq.append(
np.array([x[3] for x in data1])
)
stg.freq_text.append(
[str(x[3]*1e-6) + 'MHz' for x in data1]
)
stg.water_attenuation.append(
[x[4] for x in data1]
)
stg.kt_read = [x[5] for x in data1]
stg.kt_corrected = [x[6] for x in data1]
stg.nb_profiles.append([x[7] for x in data1])
stg.nb_profiles_per_sec.append([x[8] for x in data1])
stg.nb_cells.append([x[9] for x in data1])
stg.cell_size.append([x[10] for x in data1])
stg.pulse_length.append([x[11] for x in data1])
stg.nb_pings_per_sec.append([x[12] for x in data1])
stg.nb_pings_averaged_per_profile.append([x[13] for x in data1])
stg.nb_pings_per_sec.append(
[x[12] for x in data1]
)
stg.nb_pings_averaged_per_profile.append(
[x[13] for x in data1]
)
stg.gain_rx.append([x[14] for x in data1])
stg.gain_tx.append([x[15] for x in data1])
print(stg.acoustic_data, stg.freq, stg.water_attenuation, stg.kt_read, stg.kt_corrected, stg.nb_profiles, stg.nb_profiles_per_sec,
stg.nb_cells, stg.cell_size, stg.pulse_length, stg.nb_pings_per_sec, stg.nb_pings_averaged_per_profile,
stg.gain_rx, stg.gain_tx)
logger.debug("measure:")
logger.debug(f"- stg.acoustic_data: {stg.acoustic_data}")
logger.debug(f"- stg.freq {stg.freq}")
logger.debug(f"- stg.water_attenuation {stg.water_attenuation}")
logger.debug(f"- stg.kt_read {stg.kt_read}")
logger.debug(f"- stg.kt_corrected {stg.kt_corrected}")
logger.debug(f"- stg.nb_profiles {stg.nb_profiles}")
logger.debug(f"- stg.nb_profiles_per_sec {stg.nb_profiles_per_sec}")
logger.debug(f"- stg.nb_cells {stg.nb_cells}")
logger.debug(f"- stg.cell_size {stg.cell_size}")
logger.debug(f"- stg.pulse_length {stg.pulse_length}")
logger.debug(f"- stg.nb_pings_per_sec {stg.nb_pings_per_sec}")
logger.debug(f"- stg.nb_pings_averaged_per_profile {stg.nb_pings_averaged_per_profile}")
logger.debug(f"- stg.gain_rx {stg.gain_rx}")
logger.debug(f"- stg.gain_tx {stg.gain_tx}")
print(stg.date)
print(stg.hour)
logger.debug(f"- {stg.date}")
logger.debug(f"- {stg.hour}")
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++++
# --- Table BSRawData ---
# ++++++++++++++++++++++
print("len stg.acoustic_data ", len(stg.acoustic_data))
for j in range(len(stg.acoustic_data)):
print(f"j = {j}")
query2 = f'''SELECT acoustic_data, time, depth, BS_raw_data,
time_reshape, depth_reshape, BS_raw_data_reshape,
time_cross_section, depth_cross_section,
BS_cross_section, BS_stream_bed,
depth_bottom, val_bottom, ind_bottom,
time_noise, depth_noise, BS_noise_raw_data,
SNR_raw_data, SNR_cross_section, SNR_stream_bed,
BS_raw_data_pre_process_SNR, BS_raw_data_pre_process_average,
BS_cross_section_pre_process_SNR, BS_cross_section_pre_process_average,
BS_stream_bed_pre_process_SNR, BS_stream_bed_pre_process_average, BS_mean
FROM BSRawData WHERE (acoustic_data = {j})'''
def read_table_BS_raw_data(self):
logger.debug(f"len stg.acoustic_data: {len(stg.acoustic_data)}")
data2 = cur.execute(query2).fetchall()
print("len data2 ", len(data2))
for i in range(len(stg.acoustic_data)):
query = lambda values: f'''
SELECT
{", ".join(values)}
FROM BSRawData
WHERE (acoustic_data = {i})
'''
stg.time.append(np.frombuffer(data2[0][1], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
print("stg.time[0].shape ", stg.time[j].shape, np.frombuffer(data2[0][1], dtype=np.float64).shape)
print(stg.time)
stg.depth.append(np.frombuffer(data2[0][2], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
print("stg.depth[0].shape ", stg.depth[j].shape)
print(stg.depth)
stg.BS_raw_data.append(np.frombuffer(data2[0][3], dtype=np.float64).reshape((stg.freq[j].shape[0], stg.depth[j].shape[1], stg.time[j].shape[1])))
print("BS_raw_data ", stg.BS_raw_data[j].shape)
stg.time_reshape.append(np.frombuffer(data2[0][4], dtype=np.float64).reshape((-1, stg.freq[j].shape[0])))
print("stg.time_reshape[0].shape ", stg.time_reshape[j].shape)
stg.depth_reshape.append(np.frombuffer(data2[0][5], dtype=np.float64).reshape((-1, stg.freq[j].shape[0])))
stg.BS_raw_data_reshape.append(np.frombuffer(data2[0][6], dtype=np.float64).reshape((-1, stg.freq[j].shape[0])))
print("time cross section ", stg.time_cross_section, stg.time_cross_section == [])
self.read_table_BS_raw_data_raw(query, i)
self.read_table_BS_raw_data_cross_section(query, i)
self.read_table_BS_raw_data_bed(query, i)
self.read_table_BS_raw_data_noise(query, i)
self.read_table_BS_raw_data_SNR(query, i)
self.read_table_BS_raw_data_rest(query, i)
self.read_table_BS_raw_data_mean(query, i)
print("np.frombuffer(data2[0][9], dtype=np.float64) ", np.frombuffer(data2[0][9], dtype=np.float64))
if len(np.frombuffer(data2[0][9], dtype=np.float64)) == 0:
print("Je suis là")
stg.time_cross_section.append(np.array([]))
stg.depth_cross_section.append(np.array([]))
stg.BS_cross_section.append(np.array([]))
def read_table_BS_raw_data_raw(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"time", "depth",
"BS_raw_data",
"time_reshape", "depth_reshape",
"BS_raw_data_reshape",
]
)
)[0]
it = iter(data)
time = next(it)
depth = next(it)
BS_raw_data = next(it)
time_reshape = next(it)
depth_reshape = next(it)
BS_raw_data_reshape = next(it)
stg.time.append(
np_f64_parse(time).reshape((stg.freq[i].shape[0], -1))
)
stg.depth.append(np_f64_parse(depth).reshape(
(stg.freq[i].shape[0], -1)
))
stg.BS_raw_data.append(
np_f64_parse(BS_raw_data).reshape(
(
stg.freq[i].shape[0],
stg.depth[i].shape[1],
stg.time[i].shape[1]
)
)
)
stg.time_reshape.append(
np_f64_parse(time_reshape).reshape(
(-1, stg.freq[i].shape[0])
)
)
stg.depth_reshape.append(
np_f64_parse(depth_reshape).reshape(
(-1, stg.freq[i].shape[0])
)
)
stg.BS_raw_data_reshape.append(
np_f64_parse(BS_raw_data_reshape).reshape(
(-1, stg.freq[i].shape[0])
)
)
def read_table_BS_raw_data_cross_section(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"time_cross_section",
"depth_cross_section",
"BS_cross_section",
]
)
)[0]
it = iter(data)
time = next(it)
depth = next(it)
BS = np_f64_parse(next(it))
if len(BS) == 0:
stg.time_cross_section.append(np.array([]))
stg.depth_cross_section.append(np.array([]))
stg.BS_cross_section.append(np.array([]))
else:
stg.time_cross_section.append(
np_f64_parse(time).reshape(
(stg.freq[i].shape[0], -1)
)
)
stg.depth_cross_section.append(
np_f64_parse(depth).reshape(
(stg.freq[i].shape[0], -1)
)
)
stg.BS_cross_section.append(
BS.reshape(
(
stg.freq[i].shape[0],
stg.depth_cross_section[i].shape[1],
stg.time_cross_section[i].shape[1]
)
)
)
def read_table_BS_raw_data_bed(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"BS_stream_bed",
"depth_bottom", "val_bottom", "ind_bottom",
]
)
)[0]
it = iter(data)
BS = np_f64_parse(next(it))
depth = np_f64_parse(next(it))
val = np_f64_parse(next(it))
ind = np_f64_parse(next(it))
if len(BS) == 0:
stg.BS_stream_bed.append(np.array([]))
else:
stg.BS_stream_bed.append(
BS.reshape(
(
stg.freq[i].shape[0],
stg.depth_cross_section[i].shape[1],
stg.time_cross_section[i].shape[1]
)
)
)
if len(depth) == 0:
stg.depth_bottom.append(np.array([]))
stg.val_bottom.append([])
stg.ind_bottom.append([])
else:
stg.depth_bottom.append(depth)
stg.val_bottom.append(val.tolist())
stg.ind_bottom.append(ind.tolist())
def read_table_BS_raw_data_noise(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"time_noise", "depth_noise", "BS_noise_raw_data",
]
)
)[0]
it = iter(data)
time = next(it)
depth = next(it)
BS = np_f64_parse(next(it))
if len(BS) == 0:
stg.time_noise.append(np.array([]))
stg.depth_noise.append(np.array([]))
stg.BS_noise_raw_data.append(np.array([]))
stg.BS_noise_averaged_data.append(np.array([]))
else:
stg.time_noise.append(
np_f64_parse(time).reshape(
(stg.freq[i].shape[0], -1)
)
)
stg.depth_noise.append(
np_f64_parse(depth).reshape(
(stg.freq[i].shape[0], -1)
)
)
stg.BS_noise_raw_data.append(
BS.reshape(
(
stg.freq[i].shape[0],
stg.depth_noise[i].shape[1],
stg.time_noise[i].shape[1]
)
)
)
stg.BS_noise_averaged_data.append(np.array([]))
def read_table_BS_raw_data_SNR(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"SNR_raw_data", "SNR_cross_section", "SNR_stream_bed",
]
)
)[0]
it = iter(data)
SNR_vars = [
(stg.SNR_raw_data, stg.BS_raw_data),
(stg.SNR_cross_section, stg.BS_cross_section),
(stg.SNR_stream_bed, stg.BS_stream_bed),
]
stg.SNR_stream_bed.append(np.array([]))
for dest, resh in SNR_vars:
SNR = np_f64_parse(next(it))
if len(SNR) == 0:
dest.append(np.array([]))
else:
print("Je suis ici")
print(stg.freq[j].shape)
print(np.frombuffer(data2[0][7], dtype=np.float64).shape)
stg.time_cross_section.append(np.frombuffer(data2[0][7], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
print("time cross section ", stg.time_cross_section, stg.time_cross_section[j].shape)
stg.depth_cross_section.append(np.frombuffer(data2[0][8], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
print("depth cross section ", stg.depth_cross_section, stg.depth_cross_section[j].shape)
stg.BS_cross_section.append(np.frombuffer(data2[0][9], dtype=np.float64).reshape(
(stg.freq[j].shape[0], stg.depth_cross_section[j].shape[1], stg.time_cross_section[j].shape[1])))
dest.append(SNR.reshape(resh[i].shape))
if len(np.frombuffer(data2[0][10], dtype=np.float64)) == 0:
stg.BS_stream_bed.append(np.array([]))
def read_table_BS_raw_data_rest(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(
[
"BS_raw_data_pre_process_SNR",
"BS_raw_data_pre_process_average",
"BS_cross_section_pre_process_SNR",
"BS_cross_section_pre_process_average",
"BS_stream_bed_pre_process_SNR",
"BS_stream_bed_pre_process_average",
]
)
)[0]
BS_vars = [
(stg.BS_raw_data_pre_process_SNR, stg.BS_raw_data),
(stg.BS_raw_data_pre_process_average, stg.BS_raw_data),
(stg.BS_cross_section_pre_process_SNR, stg.BS_cross_section),
(stg.BS_cross_section_pre_process_average, stg.BS_cross_section),
(stg.BS_stream_bed_pre_process_SNR, stg.BS_stream_bed),
(stg.BS_stream_bed_pre_process_average, stg.BS_stream_bed),
]
it = iter(data)
for dest, resh in BS_vars:
BS = np_f64_parse(next(it))
if len(BS) == 0:
dest.append(np.array([]))
else:
stg.BS_stream_bed.append(np.frombuffer(data2[0][10], dtype=np.float64).reshape(
(stg.freq[j].shape[0], stg.depth_cross_section[j].shape[1], stg.time_cross_section[j].shape[1])))
dest.append(BS.reshape(resh[i].shape))
if len(np.frombuffer(data2[0][11], dtype=np.float64)) == 0:
stg.depth_bottom.append(np.array([]))
stg.val_bottom.append([])
stg.ind_bottom.append([])
else:
stg.depth_bottom.append(np.frombuffer(data2[0][11], dtype=np.float64))
stg.val_bottom.append(np.frombuffer(data2[0][12], dtype=np.float64).tolist())
stg.ind_bottom.append(np.frombuffer(data2[0][13], dtype=np.float64).tolist())
print("stg.depth_bottom ", stg.depth_bottom)
print("stg.val_bottom ", stg.val_bottom)
print("stg.ind_bottom ", stg.ind_bottom)
def read_table_BS_raw_data_mean(self, query, i):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
data = self.execute(
query(["BS_mean"])
)[0]
if len(np.frombuffer(data2[0][14], dtype=np.float64)) == 0:
stg.time_noise.append(np.array([]))
stg.depth_noise.append(np.array([]))
stg.BS_noise_raw_data.append(np.array([]))
else:
stg.time_noise.append(np.frombuffer(data2[0][14], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
stg.depth_noise.append(np.frombuffer(data2[0][15], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
stg.BS_noise_raw_data.append(np.frombuffer(data2[0][16], dtype=np.float64).reshape(
(stg.freq[j].shape[0], stg.depth_noise[j].shape[1], stg.time_noise[j].shape[1])))
BS = np_f64_parse(data[0])
if len(np.frombuffer(data2[0][17], dtype=np.float64)) == 0:
stg.SNR_raw_data.append(np.array([]))
else:
stg.SNR_raw_data.append(np.frombuffer(data2[0][17], dtype=np.float64).reshape(stg.BS_raw_data[j].shape))
if len(np.frombuffer(data2[0][18], dtype=np.float64)) == 0:
stg.SNR_cross_section.append(np.array([]))
else:
stg.SNR_cross_section.append(np.frombuffer(data2[0][18], dtype=np.float64).reshape(stg.BS_cross_section[j].shape))
if len(np.frombuffer(data2[0][19], dtype=np.float64)) == 0:
stg.SNR_stream_bed.append(np.array([]))
else:
stg.SNR_stream_bed.append(np.frombuffer(data2[0][19], dtype=np.float64).reshape(stg.BS_stream_bed[j].shape))
if len(np.frombuffer(data2[0][20], dtype=np.float64)) == 0:
stg.BS_raw_data_pre_process_SNR.append(np.array([]))
else:
stg.BS_raw_data_pre_process_SNR.append(np.frombuffer(data2[0][20], dtype=np.float64).reshape(stg.BS_raw_data[j].shape))
if len(np.frombuffer(data2[0][21], dtype=np.float64)) == 0:
stg.BS_raw_data_pre_process_average.append(np.array([]))
else:
stg.BS_raw_data_pre_process_average.append(np.frombuffer(data2[0][21], dtype=np.float64).reshape(stg.BS_raw_data[j].shape))
if len(np.frombuffer(data2[0][22], dtype=np.float64)) == 0:
stg.BS_cross_section_pre_process_SNR.append(np.array([]))
else:
stg.BS_cross_section_pre_process_SNR.append(np.frombuffer(data2[0][22], dtype=np.float64).reshape(stg.BS_cross_section[j].shape))
if len(np.frombuffer(data2[0][23], dtype=np.float64)) == 0:
stg.BS_cross_section_pre_process_average.append(np.array([]))
else:
stg.BS_cross_section_pre_process_average.append(np.frombuffer(data2[0][23], dtype=np.float64).reshape(stg.BS_cross_section[j].shape))
if len(np.frombuffer(data2[0][24], dtype=np.float64)) == 0:
stg.BS_stream_bed_pre_process_SNR.append(np.array([]))
else:
stg.BS_stream_bed_pre_process_SNR.append(np.frombuffer(data2[0][24], dtype=np.float64).reshape(stg.BS_stream_bed[j].shape))
if len(np.frombuffer(data2[0][25], dtype=np.float64)) == 0:
stg.BS_stream_bed_pre_process_average.append(np.array([]))
else:
stg.BS_stream_bed_pre_process_average.append(np.frombuffer(data2[0][25], dtype=np.float64).reshape(stg.BS_stream_bed[j].shape))
if len(np.frombuffer(data2[0][26], dtype=np.float64)) == 0:
stg.BS_mean.append(np.array([]))
else:
stg.BS_mean.append(np.frombuffer(data2[0][26], dtype=np.float64).reshape((stg.freq[j].shape[0], -1)))
print(stg.BS_mean[j].shape)
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++++
# --- Table Settings ---
# +++++++++++++++++++++++
if len(BS) == 0:
stg.BS_mean.append(np.array([]))
else:
stg.BS_mean.append(
BS.reshape(
(stg.freq[i].shape[0], -1)
)
)
def read_table_settings(self):
for s in range(len(stg.acoustic_data)):
query3 = f'''SELECT acoustic_data, temperature,
tmin_index, tmin_value, tmax_index, tmax_value,
rmin_index, rmin_value, rmax_index, rmax_value,
freq_bottom_detection_index, freq_bottom_detection_value,
SNR_filter_value, Nb_cells_to_average_BS_signal
FROM Settings WHERE (acoustic_data = {s})'''
query3 = f'''
SELECT
acoustic_data, temperature,
tmin_index, tmin_value, tmax_index, tmax_value,
rmin_index, rmin_value, rmax_index, rmax_value,
freq_bottom_detection_index, freq_bottom_detection_value,
SNR_filter_value, Nb_cells_to_average_BS_signal
FROM Settings
WHERE (acoustic_data = {s})
'''
data3 = cur.execute(query3).fetchall()
data = self.execute(query3)
x = data[0]
stg.temperature = [x[1] for x in data3][0]
stg.tmin.append([(x[2], x[3]) for x in data3])
stg.tmax.append([(x[4], x[5]) for x in data3])
stg.rmin.append([(x[6], x[7]) for x in data3])
stg.rmax.append([(x[8], x[9]) for x in data3])
stg.freq_bottom_detection.append([(x[10], x[11]) for x in data3])
stg.SNR_filter_value.append([x[12] for x in data3])
stg.Nb_cells_to_average_BS_signal.append([x[13] for x in data3])
stg.temperature = [x[1]][0]
stg.tmin.append((x[2], x[3]))
stg.tmax.append((x[4], x[5]))
stg.rmin.append((x[6], x[7]))
stg.rmax.append((x[8], x[9]))
stg.freq_bottom_detection.append((x[10], x[11]))
stg.SNR_filter_value.append(x[12])
stg.Nb_cells_to_average_BS_signal.append(x[13])
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++++++++
# --- Table Sediment File ---
# +++++++++++++++++++++++++++
logger.debug(f"stg.temperature: {stg.temperature}")
logger.debug(f"stg.tmin: {stg.tmin}")
logger.debug(f"stg.tmin: {stg.tmax}")
logger.debug(f"stg.SNR_filter_value: {stg.SNR_filter_value}")
query4 = f'''SELECT path_fine, filename_fine, radius_grain_fine, path_sand, filename_sand, radius_grain_sand,
time_column_label, distance_from_bank_column_label, depth_column_label,
Ctot_fine_column_label, D50_fine_column_label,
Ctot_sand_column_label, D50_sand_column_label
from SedimentsFile'''
def read_table_sediment_file(self):
query = f'''
SELECT
path_fine, filename_fine, radius_grain_fine,
path_sand, filename_sand, radius_grain_sand,
time_column_label, distance_from_bank_column_label,
depth_column_label,
Ctot_fine_column_label, D50_fine_column_label,
Ctot_sand_column_label, D50_sand_column_label
FROM SedimentsFile
'''
data4 = cur.execute(query4).fetchall()
data = self.execute(query)[0]
print("data4 ", data4)
stg.path_fine = data[0]
stg.filename_fine = data[1]
stg.radius_grain_fine = np.array(
np.frombuffer(data[2], dtype=np.float64)
)
stg.path_sand = data[3]
stg.filename_sand = data[4]
stg.radius_grain_sand = np.array(
np.frombuffer(data[5], dtype=np.float64)
)
stg.columns_fine = (
[data[6], data[7], data[8], data[9], data[10]]
+ list(stg.radius_grain_fine)
)
stg.columns_sand = (
[data[6], data[7], data[8], data[11], data[12]]
+ list(stg.radius_grain_sand)
)
stg.path_fine = data4[0][0]
stg.filename_fine = data4[0][1]
stg.radius_grain_fine = np.array(np.frombuffer(data4[0][2], dtype=np.float64))
stg.path_sand = data4[0][3]
stg.filename_sand = data4[0][4]
stg.radius_grain_sand = np.array(np.frombuffer(data4[0][5], dtype=np.float64))
stg.columns_fine = [data4[0][6], data4[0][7], data4[0][8], data4[0][9], data4[0][10]] + list(stg.radius_grain_fine)
stg.columns_sand = [data4[0][6], data4[0][7], data4[0][8], data4[0][11], data4[0][12]] + list(stg.radius_grain_sand)
def read_table_table_sediment_data(self):
np_f64_parse = lambda d: np.frombuffer(d, dtype=np.float64)
print("sediment file : ", stg.path_fine, stg.filename_fine, stg.path_sand, stg.filename_sand)
print(stg.radius_grain_fine, stg.radius_grain_sand)
print('stg.columns_fine ', stg.columns_fine)
query = f'''
SELECT
sample_fine_name, sample_fine_index, distance_from_bank_fine,
depth_fine, time_fine, Ctot_fine, Ctot_fine_per_cent, D50_fine,
frac_vol_fine, frac_vol_fine_cumul,
# --------------------------------------------------------------------------------------------------------------
# +++++++++++++++++++++++++++
# --- Table Sediment Data ---
# +++++++++++++++++++++++++++
sample_sand_name, sample_sand_index, distance_from_bank_sand,
depth_sand, time_sand, Ctot_sand, Ctot_sand_per_cent, D50_sand,
frac_vol_sand, frac_vol_sand_cumul
FROM SedimentsData
'''
query5 = f'''SELECT sample_fine_name, sample_fine_index, distance_from_bank_fine, depth_fine, time_fine,
Ctot_fine, Ctot_fine_per_cent, D50_fine, frac_vol_fine, frac_vol_fine_cumul,
sample_sand_name, sample_sand_index, distance_from_bank_sand, depth_sand, time_sand,
Ctot_sand, Ctot_sand_per_cent, D50_sand, frac_vol_sand, frac_vol_sand_cumul
from SedimentsData'''
data5 = cur.execute(query5).fetchall()
data = self.execute(query)
stg.frac_vol_fine = []
stg.frac_vol_fine_cumul = []
stg.frac_vol_sand = []
stg.frac_vol_sand_cumul = []
for f in range(len(data5)):
stg.sample_fine.append((data5[f][0], data5[f][1]))
stg.distance_from_bank_fine.append(data5[f][2])
stg.depth_fine.append(data5[f][3])
stg.time_fine.append(data5[f][4])
stg.Ctot_fine.append(data5[f][5])
stg.Ctot_fine_per_cent.append(data5[f][6])
stg.D50_fine.append(data5[f][7])
print("np.frombuffer(data4[f][8], dtype=np.float64) ", np.frombuffer(data5[f][8], dtype=np.float64))
stg.frac_vol_fine.append(np.frombuffer(data5[f][8], dtype=np.float64))
stg.frac_vol_fine_cumul.append(np.frombuffer(data5[f][9], dtype=np.float64))
stg.sample_sand.append((data5[f][10], data5[f][11]))
stg.distance_from_bank_sand.append(data5[f][12])
stg.depth_sand.append(data5[f][13])
stg.time_sand.append(data5[f][14])
stg.Ctot_sand.append(data5[f][15])
stg.Ctot_sand_per_cent.append(data5[f][16])
stg.D50_sand.append(data5[f][17])
stg.frac_vol_sand.append(np.frombuffer(data5[f][18], dtype=np.float64))
stg.frac_vol_sand_cumul.append(np.frombuffer(data5[f][19], dtype=np.float64))
for f in range(len(data)):
stg.sample_fine.append((data[f][0], data[f][1]))
stg.distance_from_bank_fine.append(data[f][2])
stg.depth_fine.append(data[f][3])
stg.time_fine.append(data[f][4])
stg.Ctot_fine.append(data[f][5])
stg.Ctot_fine_per_cent.append(data[f][6])
stg.D50_fine.append(data[f][7])
stg.frac_vol_fine.append(
np_f64_parse(data[f][8])
)
stg.frac_vol_fine_cumul.append(
np_f64_parse(data[f][9])
)
stg.sample_sand.append((data[f][10], data[f][11]))
stg.distance_from_bank_sand.append(data[f][12])
stg.depth_sand.append(data[f][13])
stg.time_sand.append(data[f][14])
stg.Ctot_sand.append(data[f][15])
stg.Ctot_sand_per_cent.append(data[f][16])
stg.D50_sand.append(data[f][17])
stg.frac_vol_sand.append(
np_f64_parse(data[f][18])
)
stg.frac_vol_sand_cumul.append(
np_f64_parse(data[f][19])
)
stg.frac_vol_fine = np.array(stg.frac_vol_fine)
stg.frac_vol_fine_cumul = np.array(stg.frac_vol_fine_cumul)
stg.frac_vol_sand = np.array(stg.frac_vol_sand)
stg.frac_vol_sand_cumul = np.array(stg.frac_vol_sand_cumul)
# print("data 4 : ", len(data4), data4)
print('data 5 :')
print(stg.Ctot_fine, stg.sample_sand)
print(type(stg.frac_vol_fine_cumul), stg.frac_vol_fine_cumul)
# Close database cursor
cur.close()
# Close database connection
cnx.close()
print("read table finished")
def fill_acoustic_data_tab(self):
print("start fill acoustic data tab")
# tab_adt = AcousticDataTab(self.master_widget)
print("1 AcousticDataTab ", id(AcousticDataTab))
print("tab_adt.combobox_ABS_system_choice ", self.tab.combobox_ABS_system_choice)
self.tab.combobox_ABS_system_choice.editTextChanged.connect(self.tab.ABS_system_choice)
if stg.ABS_name[0] == "AQUAscat":
self.tab.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
print("combobox_ABS_system_choice.setCurrentIndex(1)", self.tab.combobox_ABS_system_choice.itemText(1),
self.tab.combobox_ABS_system_choice.itemText(2))
else:
self.tab.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
self.tab.plot_backscattered_acoustic_signal_recording()
# app = QApplication(sys.argv)
# sys.exit(app.exec_())
def reshape_variables(self):
for i in stg.acoustic_data:
for f, _ in enumerate(stg.freq[i]):
if f == 0:
depth_temp = np.array([
stg.depth_reshape[i][np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0][0]:
np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0][1], f]
])
time_temp = np.array([
stg.time_reshape[i][
np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0], f]
])
else:
# print(np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f]))
depth_temp = np.insert(depth_temp,
depth_temp.shape[0],
stg.depth_reshape[i][np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0][0]:
np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0][1], f],
axis=0)
time_temp = np.insert(time_temp,
time_temp.shape[0],
stg.time_reshape[i][
np.where(stg.depth_reshape[i][:, f] == stg.depth_reshape[i][0, f])[0], f],
axis=0)
stg.depth.append(depth_temp)
stg.time.append(time_temp)
stg.BS_raw_data.append(np.reshape(stg.BS_raw_data_reshape[i],
(len(stg.freq[i]), stg.depth[i].shape[1], stg.time[i].shape[1])))
logger.debug(f"fine: {stg.Ctot_fine}, sand: {stg.sample_sand}")

79
View/acoustic_data_tab.py
View File

@ -733,8 +733,32 @@ class AcousticDataTab(QWidget):
# -------------------- Functions for Acoustic dataTab --------------------
def retranslate_acoustic_data_tab(self):
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
self.fileListWidget.blockSignals(True)
self.combobox_ABS_system_choice.blockSignals(True)
self.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
self.ABS_system_choice()
self.fileListWidget.addFilenames(stg.filename_BS_raw_data)
self.fill_measurements_information_groupbox()
self.fill_table()
self.plot_backscattered_acoustic_signal_recording()
self.plot_profile()
self.update_frequency_combobox()
self.water_attenuation()
self.compute_tmin_tmax()
self.compute_rmin_rmax()
self.set_range_for_spinboxes_bathymetry()
self.combobox_ABS_system_choice.blockSignals(False)
self.fileListWidget.blockSignals(False)
self.blockSignals(False)
def retranslate_acoustic_data_tab(self):
self.groupbox_info.setTitle(_translate("CONSTANT_STRING", cs.MEASUREMENTS_INFORMATION))
self.label_date_acoustic_file.setText(_translate("CONSTANT_STRING", cs.DATE) + ":")
@ -953,7 +977,7 @@ class AcousticDataTab(QWidget):
self.gridLayout_groupbox_info.addWidget(self.lineEdit_temperature, 3, 1, 1, 1, Qt.AlignLeft)
self.label_temperature_unit.show()
self.gridLayout_groupbox_info.addWidget(self.label_temperature_unit, 3, 2, 1, 1, Qt.AlignLeft)
self.temperature_value()
self.setup_temperature_value()
self.label_speed_of_sound.show()
self.gridLayout_groupbox_info.addWidget(self.label_speed_of_sound, 4, 0, 1, 1, Qt.AlignLeft)
@ -1216,25 +1240,31 @@ class AcousticDataTab(QWidget):
self.pushbutton_distance_from_ABS_to_free_surface.blockSignals(False)
def temperature_value(self):
def setup_temperature_value(self):
self.water_velocity()
self.water_attenuation()
def temperature_value(self):
if findall(r",", self.lineEdit_temperature.text()):
stg.temperature = float(self.lineEdit_temperature.text().replace(',', '.'))
self.lineEdit_temperature.setText(self.lineEdit_temperature.text().replace(',', '.'))
else:
stg.temperature = float(self.lineEdit_temperature.text())
self.lineEdit_temperature.setText(self.lineEdit_temperature.text())
self.water_velocity()
self.water_attenuation()
def water_velocity(self):
"""Computing sond speed from Bilaniuk and Wong 1993"""
temp = float(self.lineEdit_temperature.text())
C = (1.40238744 * 1e3 +
5.03836171 * float(self.lineEdit_temperature.text()) -
5.81172916 * 1e-2 * float(self.lineEdit_temperature.text()) ** 2 +
3.34638117 * 1e-4 * float(self.lineEdit_temperature.text()) ** 3 -
1.48259672 * 1e-6 * float(self.lineEdit_temperature.text()) ** 4 +
3.16585020 * 1e-9 * float(self.lineEdit_temperature.text()) ** 5)
5.03836171 * temp -
5.81172916 * 1e-2 * temp ** 2 +
3.34638117 * 1e-4 * temp ** 3 -
1.48259672 * 1e-6 * temp ** 4 +
3.16585020 * 1e-9 * temp ** 5)
stg.water_velocity = C
self.lineEdit_speed_of_sound.setText(str(round(stg.water_velocity, 2)))
@ -1286,26 +1316,29 @@ class AcousticDataTab(QWidget):
# -------- Computing water attenuation coefficient ----------- #
def water_attenuation(self):
"""Computing attenuation from François and Garrison 1982"""
temp = float(self.lineEdit_temperature.text())
file_id = self.fileListWidget.currentRow()
if self.fileListWidget.count() > 0:
stg.water_attenuation[self.fileListWidget.currentRow()].clear()
for f in stg.freq[self.fileListWidget.currentRow()]:
if float(self.lineEdit_temperature.text()) > 20:
stg.water_attenuation[file_id].clear()
for f in stg.freq[file_id]:
if temp > 20:
alpha = ((3.964 * 1e-4 -
1.146 * 1e-5 * float(self.lineEdit_temperature.text()) +
1.45 * 1e-7 * float(self.lineEdit_temperature.text()) ** 2 -
6.5 * 1e-10 * float(self.lineEdit_temperature.text()) ** 3) *
1.146 * 1e-5 * temp +
1.45 * 1e-7 * temp ** 2 -
6.5 * 1e-10 * temp ** 3) *
1e-3 * (np.log(10) / 20) * (f * 1e-3) ** 2)
else:
alpha = ((4.937 * 1e-4 -
2.59 * 1e-5 * float(self.lineEdit_temperature.text()) +
9.11 * 1e-7 * float(self.lineEdit_temperature.text()) ** 2 -
1.5 * 1e-8 * float(self.lineEdit_temperature.text()) ** 3) *
2.59 * 1e-5 * temp +
9.11 * 1e-7 * temp ** 2 -
1.5 * 1e-8 * temp ** 3) *
1e-3 * (np.log(10) / 20) * (f * 1e-3) ** 2)
stg.water_attenuation[self.fileListWidget.currentRow()].append(alpha)
stg.water_attenuation[file_id].append(alpha)
self.lineEdit_sound_attenuation.setText(
str("%.6f" % stg.water_attenuation[self.fileListWidget.currentRow()][
str("%.6f" % stg.water_attenuation[file_id][
self.combobox_frequency_information.currentIndex()]))
def open_dialog_box(self):
@ -1642,9 +1675,9 @@ class AcousticDataTab(QWidget):
stg.gain_tx.append(acoustic_data._gain_tx)
stg.water_attenuation.append([])
# --- The other acoustic variables lists are filled with empty object. ---
# --- They will be used for pre- and post-processing ---
self.initiate_setting_parameters_new_others()
def initiate_setting_parameters_new_others(self):
stg.BS_cross_section.append(np.array([]))
stg.depth_cross_section.append(np.array([]))
stg.time_cross_section.append(np.array([]))
@ -1704,6 +1737,9 @@ class AcousticDataTab(QWidget):
self.fill_measurements_information_groupbox_cells()
self.fill_measurements_information_groupbox_kt()
self.water_velocity()
self.water_attenuation()
def fill_measurements_information_groupbox_datetime(self):
file_id = self.fileListWidget.currentRow()
print("file_id ", file_id)
@ -1741,6 +1777,7 @@ class AcousticDataTab(QWidget):
.currentIndexChanged\
.connect(self.combobox_frequency_information_update)
logger.debug(f"Set temperature = {stg.temperature}")
self.lineEdit_temperature.setText(str(stg.temperature))
self.label_profiles_value.setText(

10
View/acoustic_inversion_tab.py
View File

@ -21,6 +21,7 @@
# -*- coding: utf-8 -*-
import os
import logging
import numpy as np
import pandas as pd
from math import isinf
@ -50,6 +51,7 @@ from Model.acoustic_inversion_method_high_concentration import AcousticInversion
_translate = QCoreApplication.translate
logger = logging.getLogger("acoused")
class AcousticInversionTab(QWidget):
@ -369,6 +371,14 @@ class AcousticInversionTab(QWidget):
# ------------------------------------ Functions for Acoustic Inversion Tab ----------------------------------------
# ==================================================================================================================
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
# TODO: Update all widgets
self.blockSignals(False)
def update_acoustic_data_choice(self):
self.combobox_acoustic_data_choice.clear()

14
View/mainwindow.py
View File

@ -21,6 +21,13 @@
# -*- coding: utf-8 -*-
import os
import time
import pickle
import logging
import numpy as np
import pandas as pd
from subprocess import Popen
# Form implementation generated from reading ui file 'mainwindow.ui'
#
@ -38,12 +45,7 @@ from Model.calibration_constant_kt import CalibrationConstantKt
from View.about_window import AboutWindow
import settings as stg
import numpy as np
import pandas as pd
from subprocess import Popen
import time
logger = logging.getLogger("acoused")
class Ui_MainWindow(object):
def setupUi(self, MainWindow):

15
View/note_tab.py
View File

@ -1,3 +1,5 @@
import logging
from PyQt5.QtWidgets import (
QApplication, QWidget, QVBoxLayout, QHBoxLayout,
QTextEdit, QPushButton, QSpacerItem, QSpinBox,
@ -8,6 +10,8 @@ from PyQt5.QtCore import Qt
import settings as stg
logger = logging.getLogger("acoused")
class NoteTab(QWidget):
''' This class generates a enhanced notepad in Note Tab '''
@ -17,6 +21,9 @@ class NoteTab(QWidget):
path_icon = "./icons/"
# FIXME: The note are disabled because there are never saved
widget_tab.setEnabled(False)
self.verticalLayout_main_note_tab = QVBoxLayout(widget_tab)
self.horizontalLayout_toolbar = QHBoxLayout()
@ -130,6 +137,14 @@ class NoteTab(QWidget):
## -------------------------------
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
# TODO: Update all widgets
self.blockSignals(False)
# def new_text(self):
# window = self.ui_mainwindow.tab5
# window.show()

66
View/sample_data_tab.py
View File

@ -271,6 +271,24 @@ class SampleDataTab(QWidget):
self.groupbox_plot_PSD.setTitle(_translate("CONSTANT_STRING", cs.DISTRIBUTION_PLOT))
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
self.fill_comboboxes_and_plot_transect()
self.lineEdit_fine_sediment.setText(stg.filename_fine)
self.lineEdit_fine_sediment.setToolTip(stg.path_fine)
self.fill_table_fine()
self.lineEdit_sand_sediment.setText(stg.filename_sand)
self.lineEdit_sand_sediment.setToolTip(stg.path_sand)
self.fill_table_sand()
#self.plot_sample_position_on_transect()
self.plot_total_concentration()
self.plot_PSD_fine_and_sand_sediments()
self.blockSignals(False)
def last_opened_file_path(self, priority="sand"):
lst = []
@ -317,6 +335,10 @@ class SampleDataTab(QWidget):
self.lineEdit_fine_sediment.setToolTip(stg.path_fine)
self.fill_table_fine()
self.plot_sample_position_on_transect()
self.plot_total_concentration()
self.plot_PSD_fine_and_sand_sediments()
def open_dialog_box_sand_sediment(self):
filename_sand_sediment = QFileDialog.getOpenFileName(
self, "Sand sediment file",
@ -342,6 +364,10 @@ class SampleDataTab(QWidget):
self.lineEdit_sand_sediment.setToolTip(stg.path_sand)
self.fill_table_sand()
self.plot_sample_position_on_transect()
self.plot_total_concentration()
self.plot_PSD_fine_and_sand_sediments()
def load_fine_sediment_data(self):
fine_granulo_data = GranuloLoader(
os.path.join(stg.path_fine, stg.filename_fine)
@ -396,7 +422,7 @@ class SampleDataTab(QWidget):
horizontal_header = list(
itertools.chain(
["Color", "Sample"],
list(map(str, stg.columns_fine[[0, 2]])),
[str(stg.columns_fine[0]), str(stg.columns_fine[2])],
list(map(str, stg.columns_fine[3:]))
)
)
@ -472,10 +498,6 @@ class SampleDataTab(QWidget):
self.combobox_x_axis.currentIndexChanged.connect(self.plot_total_concentration)
self.combobox_y_axis.currentIndexChanged.connect(self.plot_total_concentration)
self.plot_sample_position_on_transect()
self.plot_total_concentration()
self.plot_PSD_fine_and_sand_sediments()
self.tableWidget_fine.blockSignals(False)
else:
msgBox = QMessageBox()
@ -495,7 +517,7 @@ class SampleDataTab(QWidget):
horizontal_header = list(
itertools.chain(
["Color", "Sample"],
list(map(str, stg.columns_sand[[0, 2]])),
[str(stg.columns_fine[0]), str(stg.columns_fine[2])],
list(map(str, stg.columns_sand[3:]))
)
)
@ -574,10 +596,6 @@ class SampleDataTab(QWidget):
self.combobox_y_axis.currentIndexChanged\
.connect(self.plot_total_concentration)
self.plot_sample_position_on_transect()
self.plot_total_concentration()
self.plot_PSD_fine_and_sand_sediments()
self.tableWidget_sand.blockSignals(False)
# --- Function to extract position of sample from table checkboxes to update plots ---
@ -738,13 +756,19 @@ class SampleDataTab(QWidget):
self.combobox_acoustic_data.clear()
for n, m in enumerate(stg.noise_method):
if stg.noise_method[n] == 0:
self.combobox_acoustic_data.addItem(stg.filename_BS_raw_data[n])
elif stg.noise_method[n]!=0:
self.combobox_acoustic_data.addItem(stg.data_preprocessed[n])
self.combobox_acoustic_data\
.addItem(stg.filename_BS_raw_data[n])
elif stg.noise_method[n] != 0:
self.combobox_acoustic_data\
.addItem(stg.data_preprocessed[n])
self.plot_sample_position_on_transect()
self.combobox_acoustic_data.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
self.combobox_frequencies.currentIndexChanged.connect(self.update_plot_sample_position_on_transect)
self.combobox_acoustic_data\
.currentIndexChanged\
.connect(self.update_plot_sample_position_on_transect)
self.combobox_frequencies\
.currentIndexChanged\
.connect(self.update_plot_sample_position_on_transect)
def plot_sample_position_on_transect(self):
self.verticalLayout_groupbox_plot_transect\
@ -998,7 +1022,7 @@ class SampleDataTab(QWidget):
self.axis_plot_sample_position_on_transect.text(
1, .85, stg.freq_text[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex()],
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_plot_sample_position_on_transect.transAxes)
@ -1006,11 +1030,11 @@ class SampleDataTab(QWidget):
self.axis_plot_sample_position_on_transect.tick_params(axis='both', labelsize=8)
self.axis_plot_sample_position_on_transect.text(.98, .03, "Time (sec)",
fontsize=10, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
fontsize=10, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.9,
horizontalalignment='right', verticalalignment='bottom', rotation='horizontal',
transform=self.axis_plot_sample_position_on_transect.transAxes)
self.axis_plot_sample_position_on_transect.text(.04, .53, "Depth (m)",
fontsize=10, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.9,
fontsize=10, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.9,
horizontalalignment='right', verticalalignment='bottom', rotation='vertical',
transform=self.axis_plot_sample_position_on_transect.transAxes)
@ -1324,7 +1348,7 @@ class SampleDataTab(QWidget):
self.axis_plot_sample_position_on_transect.text(
1, .85, stg.freq_text[self.combobox_acoustic_data.currentIndex()][self.combobox_frequencies.currentIndex()],
fontsize=14, fontweight='bold', fontname="Ubuntu", c="black", alpha=0.5,
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_plot_sample_position_on_transect.transAxes)
@ -1332,13 +1356,13 @@ class SampleDataTab(QWidget):
self.axis_plot_sample_position_on_transect.tick_params(axis='both', labelsize=8)
self.axis_plot_sample_position_on_transect.text(.98, .03, "Time (sec)",
fontsize=10, fontweight='bold', fontname="Ubuntu", c="black",
fontsize=10, fontweight='bold', fontname="DejaVu Sans", c="black",
alpha=0.9,
horizontalalignment='right', verticalalignment='bottom',
rotation='horizontal',
transform=self.axis_plot_sample_position_on_transect.transAxes)
self.axis_plot_sample_position_on_transect.text(.04, .53, "Depth (m)",
fontsize=10, fontweight='bold', fontname="Ubuntu", c="black",
fontsize=10, fontweight='bold', fontname="DejaVu Sans", c="black",
alpha=0.9,
horizontalalignment='right', verticalalignment='bottom',
rotation='vertical',

12
View/sediment_calibration_tab.py
View File

@ -857,6 +857,14 @@ class SedimentCalibrationTab(QWidget):
# ----------------------------------- Functions for Signal processing Tab --------------------------------------
# ==============================================================================================================
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
self.function_pushbutton_update_acoustic_file()
self.blockSignals(False)
def function_pushbutton_update_acoustic_file(self):
if len(stg.data_preprocessed) == 0:
return
@ -1776,10 +1784,6 @@ class SedimentCalibrationTab(QWidget):
)
def update_label_kt_value_for_calibration(self):
print("self.combobox_freq1.currentIndex() ",
self.combobox_freq1.currentIndex(),
self.combobox_freq1.currentText())
freq_1 = self.combobox_freq1.currentIndex()
freq_2 = self.combobox_freq2.currentIndex()

507
View/signal_processing_tab.py
View File

@ -511,12 +511,31 @@ class SignalProcessingTab(QWidget):
self.icon_clear = QIcon(path_icon("clear.png"))
self.icon_apply = QIcon(path_icon("circle_green_arrow_right.png"))
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
self.combobox_acoustic_data_choice.blockSignals(True)
# --------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# +++++++++ FUNCTION +++++++++
# --------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------
self.full_update_fill_text()
self.update_SignalPreprocessingTab()
self.combobox_acoustic_data_choice.blockSignals(False)
self.blockSignals(False)
def full_update_fill_text(self):
data_id = self.combobox_acoustic_data_choice.currentIndex()
self.lineEdit_profile_tail_value.setText(
str(stg.noise_value[data_id])
)
self.lineEdit_SNR_criterion.setText(
str(stg.SNR_filter_value[data_id])
)
self.lineEdit_horizontal_average.setText(
str(stg.Nb_cells_to_average_BS_signal[data_id])
)
def update_SignalPreprocessingTab(self):
@ -564,6 +583,33 @@ class SignalProcessingTab(QWidget):
self.combobox_freq_noise_from_profile_tail.blockSignals(False)
self.combobox_acoustic_data_choice.blockSignals(False)
def _is_correct_shape(self, data):
data_id = self.combobox_acoustic_data_choice.currentIndex()
if stg.time_cross_section[data_id].shape != (0,):
x_time = stg.time_cross_section[data_id]
else:
x_time = stg.time[data_id]
if stg.depth_cross_section[data_id].shape != (0,):
y_depth = stg.depth_cross_section[data_id]
else:
y_depth = stg.depth[data_id]
time_shape, = x_time[data_id].shape
depth_shape, = y_depth[data_id].shape
logger.debug(f"_is_correct_shape: time shape: {time_shape}")
logger.debug(f"_is_correct_shape: depth shape: {depth_shape}")
logger.debug(f"_is_correct_shape: data shape: {data[data_id].shape}")
if data[data_id].shape == (0,):
return False
_, y, z = data[data_id].shape
return (y == depth_shape and z == time_shape)
def recompute(self):
data_id = self.combobox_acoustic_data_choice.currentIndex()
@ -784,9 +830,17 @@ class SignalProcessingTab(QWidget):
self.compute_average_profile_tail()
self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()]))
self.lineEdit_SNR_criterion.setText(
str(stg.SNR_filter_value[
self.combobox_acoustic_data_choice.currentIndex()
])
)
self.lineEdit_horizontal_average.setText(str(stg.Nb_cells_to_average_BS_signal[self.combobox_acoustic_data_choice.currentIndex()]))
self.lineEdit_horizontal_average.setText(
str(stg.Nb_cells_to_average_BS_signal[
self.combobox_acoustic_data_choice.currentIndex()
])
)
self.combobox_frequency_profile.clear()
self.combobox_frequency_profile.addItems(
@ -808,28 +862,29 @@ class SignalProcessingTab(QWidget):
if len(stg.filename_BS_raw_data) == 0:
pass
else:
data_id = self.combobox_acoustic_data_choice.currentIndex()
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] = 0
stg.BS_noise_raw_data[data_id] = np.array([])
stg.BS_noise_averaged_data[data_id] = np.array([])
stg.SNR_raw_data[data_id] = np.array([])
stg.SNR_cross_section[data_id] = np.array([])
stg.SNR_stream_bed[data_id] = np.array([])
stg.time_noise[data_id] = np.array([])
stg.SNR_filter_value[data_id] = 0
stg.BS_raw_data_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_raw_data_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_raw_data_pre_process_SNR[data_id] = np.array([])
stg.BS_raw_data_pre_process_average[data_id] = np.array([])
stg.BS_cross_section_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_cross_section_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_cross_section_pre_process_SNR[data_id] = np.array([])
stg.BS_cross_section_pre_process_average[data_id] = np.array([])
stg.BS_stream_bed_pre_process_SNR[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_stream_bed_pre_process_average[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
print("stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()]", stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()])
if stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 0:
stg.BS_stream_bed_pre_process_SNR[data_id] = np.array([])
stg.BS_stream_bed_pre_process_average[data_id] = np.array([])
print("stg.noise_method[data_id]", stg.noise_method[data_id])
if stg.noise_method[data_id] == 0:
self.lineEdit_noise_file.clear()
elif stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] == 1:
elif stg.noise_method[data_id] == 1:
self.lineEdit_val1.clear()
self.lineEdit_val1.setText("0.00")
@ -946,136 +1001,140 @@ class SignalProcessingTab(QWidget):
def load_noise_data_and_compute_SNR(self):
data_id = self.combobox_acoustic_data_choice.currentIndex()
stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 0
stg.noise_method[data_id] = 0
noise_data = AcousticDataLoader(stg.path_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()] +
noise_data = AcousticDataLoader(stg.path_BS_noise_data[data_id] +
"/" +
stg.filename_BS_noise_data[self.combobox_acoustic_data_choice.currentIndex()])
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._BS_raw_data
stg.filename_BS_noise_data[data_id])
stg.BS_noise_raw_data[data_id] = noise_data._BS_raw_data
stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._time
stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = noise_data._r
stg.time_noise[data_id] = noise_data._time
stg.depth_noise[data_id] = noise_data._r
if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
if stg.BS_stream_bed[data_id].shape != (0,):
noise = np.zeros(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape)
noise = np.zeros(stg.BS_stream_bed[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] -
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_stream_bed[data_id] = (
np.divide((stg.BS_stream_bed[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
elif stg.BS_cross_section[data_id].shape != (0,):
noise = np.zeros(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape)
noise = np.zeros(stg.BS_cross_section[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()] -
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_cross_section[data_id] = (
np.divide((stg.BS_cross_section[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
# stg.SNR_reshape = np.reshape(stg.SNR_cross_section, (stg.r.shape[1] * stg.t.shape[1], stg.freq.shape[0]), order="F")
else:
noise = np.zeros(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape)
noise = np.zeros(stg.BS_raw_data[data_id].shape)
for f, _ in enumerate(noise_data._freq):
noise[f, :, :] = np.mean(
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = noise
stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()] -
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
stg.BS_noise_raw_data[data_id][f, :, :], axis=(0, 1))
stg.BS_noise_averaged_data[data_id] = noise
stg.SNR_raw_data[data_id] = (
np.divide((stg.BS_raw_data[data_id] -
stg.BS_noise_averaged_data[data_id]) ** 2,
stg.BS_noise_averaged_data[data_id] ** 2))
def open_plot_noise_window(self):
pnw = PlotNoiseWindow()
pnw.exec()
def compute_noise_from_profile_tail_value(self):
data_id = self.combobox_acoustic_data_choice.currentIndex()
stg.noise_method[self.combobox_acoustic_data_choice.currentIndex()] = 1
stg.noise_method[data_id] = 1
stg.noise_value[data_id] = (
float(self.lineEdit_profile_tail_value.text().replace(",", "."))
)
stg.noise_value[self.combobox_acoustic_data_choice.currentIndex()] = (
float(self.lineEdit_profile_tail_value.text().replace(",", ".")))
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
if stg.time_cross_section[data_id].shape != (0,):
stg.time_noise[data_id] = (
stg.time_cross_section[data_id]
)
else:
stg.time_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.time[self.combobox_acoustic_data_choice.currentIndex()])
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()])
stg.time_noise[data_id] = (
stg.time[data_id]
)
if stg.depth_cross_section[data_id].shape != (0,):
stg.depth_noise[data_id] = (
stg.depth_cross_section[data_id]
)
else:
stg.depth_noise[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.depth[self.combobox_acoustic_data_choice.currentIndex()])
stg.depth_noise[data_id] = (
stg.depth[data_id]
)
# --- Compute noise from value and compute SNR ---
if stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = np.array([])
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
np.full(stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape,
if self._is_correct_shape(stg.BS_stream_bed):
stg.BS_noise_raw_data[data_id] = np.array([])
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_stream_bed[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
:stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_stream_bed[self.combobox_acoustic_data_choice.currentIndex()]
- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id][:, :,
:stg.BS_stream_bed[data_id].shape[2]])
stg.SNR_stream_bed[data_id] = (
np.divide((stg.BS_stream_bed[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2))
elif stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
np.full(stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape,
elif self._is_correct_shape(stg.BS_cross_section):
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_cross_section[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()][:, :,
:stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[2]])
stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_cross_section[self.combobox_acoustic_data_choice.currentIndex()]
- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2)) #
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id][:, :,
:stg.BS_cross_section[data_id].shape[2]])
stg.SNR_cross_section[data_id] = (
np.divide((stg.BS_cross_section[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2)) #
else:
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
np.full(stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape,
stg.BS_noise_raw_data[data_id] = (
np.full(stg.BS_raw_data[data_id].shape,
float(self.lineEdit_profile_tail_value.text().replace(",", "."))))
stg.BS_noise_averaged_data[self.combobox_acoustic_data_choice.currentIndex()] = (
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()])
stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()] = (
np.divide((stg.BS_raw_data[self.combobox_acoustic_data_choice.currentIndex()]
- stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()]) ** 2,
stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()] ** 2))
stg.BS_noise_averaged_data[data_id] = (
stg.BS_noise_raw_data[data_id])
stg.SNR_raw_data[data_id] = (
np.divide((stg.BS_raw_data[data_id]
- stg.BS_noise_raw_data[data_id]) ** 2,
stg.BS_noise_raw_data[data_id] ** 2))
self.combobox_frequency_profile.clear()
self.combobox_frequency_profile.addItems(
[f for f in stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()]])
[f for f in stg.freq_text[data_id]])
# --- Trigger graphic widgets ---
if stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()] == 0:
if stg.SNR_filter_value[data_id] == 0:
self.lineEdit_SNR_criterion.setText("0.00")
else:
self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[self.combobox_acoustic_data_choice.currentIndex()]))
self.lineEdit_SNR_criterion.setText(str(stg.SNR_filter_value[data_id]))
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
self.slider.setMaximum(stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
if stg.time_cross_section[data_id].shape != (0,):
self.slider.setMaximum(stg.time_cross_section[data_id].shape[1])
else:
self.slider.setMaximum(stg.time[self.combobox_acoustic_data_choice.currentIndex()].shape[1])
self.slider.setMaximum(stg.time[data_id].shape[1])
# self.activate_list_of_pre_processed_data()
@ -1123,14 +1182,17 @@ class SignalProcessingTab(QWidget):
# elif self.canvas_SNR == None:
else:
data_id = self.combobox_acoustic_data_choice.currentIndex()
if ((self.combobox_acoustic_data_choice.currentIndex() != -1)
and (stg.BS_noise_raw_data[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,))):
if ((data_id != -1)
and (stg.BS_noise_raw_data[data_id].shape != (0,))):
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
self.fig_SNR, self.axis_SNR = plt.subplots(nrows=stg.freq[self.combobox_acoustic_data_choice.currentIndex()].shape[0], ncols=1, sharex=True, sharey=False, layout='constrained')
self.fig_SNR, self.axis_SNR = plt.subplots(
nrows=stg.freq[data_id].shape[0], ncols=1,
sharex=True, sharey=False, layout='constrained'
)
self.canvas_SNR = FigureCanvas(self.fig_SNR)
self.toolbar_SNR = NavigationToolBar(self.canvas_SNR, self)
@ -1139,158 +1201,82 @@ class SignalProcessingTab(QWidget):
self.verticalLayout_groupbox_plot_SNR.addWidget(self.toolbar_SNR)
self.verticalLayout_groupbox_plot_SNR.addWidget(self.scroll_SNR)
for f, _ in enumerate(stg.freq[self.combobox_acoustic_data_choice.currentIndex()]):
if stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
else:
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
val_min = np.nanmin(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
val_max = np.nanmax(stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
cf = (self.axis_SNR[f].contourf(x, -y,
stg.SNR_stream_bed[self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
levels, cmap='gist_rainbow',
norm=norm))
elif stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
if stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
else:
if stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :])
elif stg.depth[self.combobox_acoustic_data_choice.currentIndex()].shape != (0,):
x, y = np.meshgrid(
stg.time[self.combobox_acoustic_data_choice.currentIndex()][f, :],
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][f, :])
val_min = np.nanmin(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
val_max = np.nanmax(stg.SNR_cross_section[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
cf = (self.axis_SNR[f].contourf(x, -y,
stg.SNR_cross_section[
self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
levels, cmap='gist_rainbow', norm=norm))
for f, _ in enumerate(stg.freq[data_id]):
if stg.SNR_stream_bed[data_id].shape != (0,):
SNR_data = stg.SNR_stream_bed
elif stg.SNR_cross_section[data_id].shape != (0,):
SNR_data = stg.SNR_cross_section
if stg.time_cross_section[data_id].shape != (0,):
time_data = stg.time_cross_section
else:
time_data = stg.time
x, y = np.meshgrid(stg.time[self.combobox_acoustic_data_choice.currentIndex()][0, :],
stg.depth[self.combobox_acoustic_data_choice.currentIndex()][0, :])
if stg.depth_cross_section[data_id].shape != (0,):
depth_data = stg.depth_cross_section
elif stg.depth[data_id].shape != (0,):
depth_data = stg.depth
val_min = np.nanmin(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
val_max = np.nanmax(stg.SNR_raw_data[self.combobox_acoustic_data_choice.currentIndex()][f, :, :])
if val_min == val_max:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
x, y = np.meshgrid(
time_data[data_id][f, :],
depth_data[data_id][f, :]
)
val_min = np.nanmin(SNR_data[data_id][f, :, :])
val_max = np.nanmax(SNR_data[data_id][f, :, :])
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
if val_min != val_max:
if val_min == 0:
val_min = 1e-5
else:
if val_min == 0:
val_min = 1e-5
if val_max > 1000:
levels = np.array([00.1, 1, 2, 10, 100, 1000, 1e6])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1.2]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
else:
levels = np.array([00.1, 1, 2, 10, 100, 1000, val_max*1000 + 1])
bounds = [00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
levels = np.array(
[00.1, 1, 2, 10, 100, 1000, val_max * 1000 + 1]
)
bounds = [
00.1, 1, 2, 10, 100, 1000,
val_max * 1000 + 1
]
cf = (self.axis_SNR[f].contourf(x, -y,
stg.SNR_raw_data[
self.combobox_acoustic_data_choice.currentIndex()][f, :, :],
levels, cmap='gist_rainbow', norm=norm))
norm = BoundaryNorm(boundaries=bounds, ncolors=300)
cf = self.axis_SNR[f].contourf(
x, -y,
SNR_data[data_id][f, :, :],
levels, cmap='gist_rainbow',
norm=norm
)
self.axis_SNR[f].text(1, .70, stg.freq_text[self.combobox_acoustic_data_choice.currentIndex()][f],
fontsize=14, fontweight='bold', fontname="DejaVu Sans", c="black", alpha=0.5,
horizontalalignment='right', verticalalignment='bottom',
transform=self.axis_SNR[f].transAxes)
self.axis_SNR[f].text(
1, .70, stg.freq_text[data_id][f],
fontsize=14, fontweight='bold', fontname="DejaVu Sans",
c="black", alpha=0.5,
horizontalalignment='right',
verticalalignment='bottom',
transform=self.axis_SNR[f].transAxes
)
self.fig_SNR.supxlabel('Time (sec)', fontsize=10)
self.fig_SNR.supylabel('Depth (m)', fontsize=10)
cbar = self.fig_SNR.colorbar(cf, ax=self.axis_SNR[:], shrink=1, location='right')
cbar.set_label(label='Signal to Noise Ratio', rotation=270, labelpad=10)
cbar.set_ticklabels(['0', '1', '2', '10', '100', r'10$^3$', r'10$^6$'])
cbar = self.fig_SNR.colorbar(
cf, ax=self.axis_SNR[:],
shrink=1, location='right'
)
cbar.set_label(
label='Signal to Noise Ratio',
rotation=270, labelpad=10
)
cbar.set_ticklabels(
[
'0', '1', '2', '10', '100',
r'10$^3$', r'10$^6$'
]
)
self.fig_SNR.canvas.draw_idle()
else:
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.toolbar_SNR)
self.verticalLayout_groupbox_plot_SNR.removeWidget(self.scroll_SNR)
@ -1412,9 +1398,6 @@ class SignalProcessingTab(QWidget):
x_time = stg.time[data_id]
y_depth = stg.depth[data_id]
logger.debug(f"x_time: {x_time[data_id].shape}")
logger.debug(f"y_depth: {y_depth[data_id].shape}")
for f, _ in enumerate(stg.freq[data_id]):
if stg.BS_stream_bed_pre_process_average[data_id].shape != (0,):
BS_data = stg.BS_stream_bed_pre_process_average
@ -1435,8 +1418,6 @@ class SignalProcessingTab(QWidget):
elif stg.BS_raw_data[data_id].shape != (0,):
BS_data = stg.BS_raw_data
logger.debug(f"BS_data: {BS_data[data_id].shape}")
val_min = np.nanmin(
BS_data[data_id][f, :, :]
)
@ -1539,20 +1520,10 @@ class SignalProcessingTab(QWidget):
)
)
if stg.time_cross_section[data_id].shape != (0,):
if stg.depth_cross_section[data_id].shape != (0,):
x_time = stg.time_cross_section[data_id]
y_depth = stg.depth_cross_section[data_id]
elif stg.depth[data_id].shape != (0,):
x_time = stg.time_cross_section[data_id]
y_depth = stg.depth[data_id]
else:
if stg.depth_cross_section[data_id].shape != (0,):
x_time = stg.time[data_id]
y_depth = stg.depth_cross_section[data_id]
elif stg.depth[data_id].shape != (0,):
x_time = stg.time[data_id]
y_depth = stg.depth[data_id]
if stg.depth_cross_section[data_id].shape != (0,):
y_depth = stg.depth_cross_section[data_id]
elif stg.depth[data_id].shape != (0,):
y_depth = stg.depth[data_id]
BS = [
stg.BS_stream_bed_pre_process_SNR,
@ -1572,11 +1543,6 @@ class SignalProcessingTab(QWidget):
stg.BS_raw_data_pre_process_average,
]
time_shape, = x_time[data_id].shape
depth_shape, = y_depth[data_id].shape
logger.debug(f"time_shape: {time_shape}")
logger.debug(f"depth_shape: {depth_shape}")
BS_data = stg.BS_raw_data
BS_data_ppa = stg.BS_raw_data_pre_process_average
for i in range(len(BS)):
@ -1585,8 +1551,7 @@ class SignalProcessingTab(QWidget):
if bs[data_id].shape == (0,):
continue
x, y, z = bs[data_id].shape
if y == depth_shape and z == time_shape:
if self._is_correct_shape(bs):
BS_data = bs
BS_data_ppa = BS_ppa[i]
break

5
View/user_manual_tab.py
View File

@ -27,5 +27,10 @@ class UserManualTab(QWidget):
# self.label_picture_theory.resize(np.int(pic.width()/100), np.int(pic.height()/100))
# self.verticalLayout_main.addWidget(self.label_picture_theory)
def full_update(self):
logger.debug(f"{__name__}: Update")
self.blockSignals(True)
# TODO: Update all widgets
self.blockSignals(False)

24
main.py
View File

@ -95,6 +95,15 @@ class MainApplication(QMainWindow):
# self.user_manual_tab = UserManualTab(self.ui_mainwindow.tab7)
self.tabs = [
self.acoustic_data_tab,
self.signal_processing_tab,
self.sample_data_tab,
self.sediment_calibration_tab,
self.acoustic_inversion_tab,
self.note_tab
]
# **************************************************
# ---------------- Text File Error -----------------
@ -108,14 +117,17 @@ class MainApplication(QMainWindow):
# traceback.TracebackException.from_exception(e).print(file=sortie)
def open_study_update_tabs(self):
self.acoustic_data_tab.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
self.acoustic_data_tab.fileListWidget.addFilenames(stg.filename_BS_raw_data)
for tab in self.tabs:
tab.full_update()
self.signal_processing_tab.combobox_acoustic_data_choice.addItems(stg.filename_BS_raw_data)
# self.acoustic_data_tab.combobox_ABS_system_choice.setCurrentText(stg.ABS_name[0])
# self.acoustic_data_tab.fileListWidget.addFilenames(stg.filename_BS_raw_data)
self.sample_data_tab.fill_comboboxes_and_plot_transect()
self.sample_data_tab.lineEdit_fine_sediment.setText(stg.filename_fine)
self.sample_data_tab.lineEdit_fine_sediment.setToolTip(stg.path_fine)
# self.signal_processing_tab.combobox_acoustic_data_choice.addItems(stg.filename_BS_raw_data)
# self.sample_data_tab.fill_comboboxes_and_plot_transect()
# self.sample_data_tab.lineEdit_fine_sediment.setText(stg.filename_fine)
# self.sample_data_tab.lineEdit_fine_sediment.setToolTip(stg.path_fine)
# self.sample_data_tab.fill_table_fine()
if __name__ == '__main__':