from Model.AquascatDataLoader import RawAquascatData

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

path_BS_raw_data = "/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/" \
                        "Data/Acoustic_data/20180107123500.aqa"
path_noise_data = "/home/bmoudjed/Documents/3 SSC acoustic meas project/Graphical interface project/" \
                       "Data/AcousticNoise_data/20180107121600.aqa"

class AcousticDataLoader():

    def __init__(self, path_BS_raw_data: str):

        self.path_BS_raw_data = path_BS_raw_data

        # --- Load Backscatter acoustic raw data with RawAquascatData class ---

        self._data_BS = RawAquascatData(self.path_BS_raw_data)

        self._BS_raw_data = self._data_BS.V
        self._r = self._data_BS.r
        self._freq = self._data_BS.Freq
        self._freq_text = self._data_BS.freqText
        self._time = np.array([t / self._data_BS.PingRate for t in range(self._data_BS.NumProfiles)])

        self._date = self._data_BS.date.date()
        self._hour = self._data_BS.date.time()
        self._nb_profiles = self._data_BS.NumProfiles
        self._nb_profiles_per_sec = self._data_BS.ProfileRate
        self._nb_cells = self._data_BS.NumCells
        self._cell_size = self._data_BS.cellSize
        self._pulse_length = self._data_BS.TxPulseLength
        self._nb_pings_per_sec = self._data_BS.PingRate
        self._nb_pings_averaged_per_profile = self._data_BS.Average
        self._kt = self._data_BS.Kt
        self._gain_rx = self._data_BS.RxGain
        self._gain_tx = self._data_BS.TxGain

        self._snr = np.array([])
        self._snr_reshape = np.array([])
        self._time_snr = np.array([])

        # print(["BS - " + f for f in self._freq_text])
        # print(self._time.shape[0]*self._r.shape[0]*4)

        # fig, ax = plt.subplots(nrows=1, ncols=1)
        # ax.pcolormesh(self._time, self._r, np.flipud(self._BS_raw_data[:, 1, :]),
        #               cmap='viridis',
        #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
        # ax.pcolormesh(range(self._BS_raw_data.shape[2]), range(self._BS_raw_data.shape[0]), self._BS_raw_data[:, 1, :], cmap='viridis',
        #               norm=LogNorm(vmin=1e-5, vmax=np.max(self._BS_raw_data[:, 0, :])))  # , shading='gouraud')
        # plt.show()


        # print(self.reshape_BS_raw_cross_section()[0, 0])
        # self.reshape_r()
        # self.reshape_t()

        # dataframe = self.concatenate_data()
        # print(dataframe)

    def reshape_BS_raw_cross_section(self):
        BS_raw_cross_section = np.reshape(self._BS_raw_data,
                                          (self._r.shape[0]*len(self._time), self._freq.shape[0]),
                                          order="F")
        return BS_raw_cross_section

    def reshape_r(self):
        r = np.reshape(np.repeat(self._r, self._time.shape[0], axis=1),
                       self._r.shape[0]*self._time.shape[0],
                       order="F")
        return r

    def reshape_t(self):
        t = np.reshape(np.repeat(self._time, self._r.shape[0]), (self._time.shape[0]*self._r.shape[0], 1))
        return t

    def concatenate_data(self):
        self.reshape_t()
        self.reshape_BS_raw_cross_section()
        # print(self.reshape_t().shape)
        # print(se.lf.reshape_BS_raw_cross_section().shape)
        df = pd.DataFrame(np.concatenate((self.reshape_t(), self.reshape_BS_raw_cross_section()), axis=1),
                          columns=["time"] + self._freq_text)
        return df


# if __name__ == "__main__":
#     AcousticDataLoader(path_BS_raw_data)