# ============================================================================== #
    # 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):
        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
        '''

        data = self.execute(query)[0]

        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)
        )

    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])))