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acoused
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acoused/Model/read_table_for_open.py

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# ============================================================================== #
# read_table_for_open.py - AcouSed #
# Copyright (C) 2024 INRAE #
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <https://www.gnu.org/licenses/>. #
# by Brahim MOUDJED #
# ============================================================================== #
# -*- coding: utf-8 -*-
import os
import sys
import sqlite3
import logging
import numpy as np
from PyQt5.QtWidgets import QFileDialog, QApplication, QWidget, QTabWidget
import settings as stg
from settings import BS_raw_data, acoustic_data
from View.acoustic_data_tab import AcousticDataTab
logger = logging.getLogger("acoused")
class ReadTableForOpen:
def __init__(self):
self.opened = False
self.open_file_dialog()
def open_file_dialog(self):
name, _ = QFileDialog.getOpenFileName(
caption="Open Acoused file",
directory="",
filter="Acoused file (*.acd)",
options=QFileDialog.DontUseNativeDialog
)
if name != "":
stg.dirname_open = os.path.dirname(name)
stg.filename_open = os.path.basename(name)
try:
os.chdir(stg.dirname_open)
except OSError as e:
logger.warning(f"chdir: {str(e)}")
self.read_table()
self.opened = True
def execute(self, query):
return self._cur.execute(query).fetchall()
def read_table(self):
stg.read_table_trigger = 1
logger.debug(f"Open '{stg.filename_open}'")
cnx = sqlite3.connect(stg.filename_open)
self._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()
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 = self.execute(query0)
logger.debug(f"data0: {data0}")
stg.acoustic_data = [x[0] for x in data0]
logger.debug(f"stg.acoustic_data: {stg.acoustic_data}")
for k in range(len(stg.acoustic_data)):
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)
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])
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)):
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.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.gain_rx.append([x[14] for x in data1])
stg.gain_tx.append([x[15] for x in data1])
logger.debug("measure:")
logger.debug(f"- {stg.acoustic_data}")
logger.debug(f"- {stg.freq}")
logger.debug(f"- {stg.water_attenuation}")
logger.debug(f"- {stg.kt_read}")
logger.debug(f"- {stg.kt_corrected}")
logger.debug(f"- {stg.nb_profiles}")
logger.debug(f"- {stg.nb_profiles_per_sec}")
logger.debug(f"- {stg.nb_cells}")
logger.debug(f"- {stg.cell_size}")
logger.debug(f"- {stg.pulse_length}")
logger.debug(f"- {stg.nb_pings_per_sec}")
logger.debug(f"- {stg.nb_pings_averaged_per_profile}")
logger.debug(f"- {stg.gain_rx}")
logger.debug(f"- {stg.gain_tx}")
logger.debug(f"- {stg.date}")
logger.debug(f"- {stg.hour}")
def read_table_BS_raw_data(self):
logger.debug(f"len stg.acoustic_data: {len(stg.acoustic_data)}")
for i in range(len(stg.acoustic_data)):
query = lambda values: f'''
SELECT
{", ".join(values)}
FROM BSRawData
WHERE (acoustic_data = {i})
'''
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)
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([]))
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]
)
)
)
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_cross_section, stg.BS_stream_bed),
]
for dest, resh in SNR_vars:
SNR = np_f64_parse(next(it))
if len(SNR) == 0:
dest.append(np.array([]))
else:
dest.append(SNR.reshape(resh[i].shape))
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:
dest.append(BS.reshape(resh[i].shape))
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]
BS = np_f64_parse(data[0])
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})'''
data3 = self.execute(query3)
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])
def read_table_sediment_file(self):
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'''
data4 = self.execute(query4)
print("data4 ", data4)
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)
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)
def read_table_table_sediment_data(self):
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 = self.execute(query5)
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))
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)
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])))
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