import scipy
from scipy import signal
import os
import lmdb
import pickle
import numpy as np
import mne

tasks = ['04', '06', '08', '10', '12', '14'] # select the data for motor imagery

root_dir = '/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/files'
files = [file for file in os.listdir(root_dir)]
files = sorted(files)

files_dict = {
    'train': files[:70],
    'val': files[70:89],
    'test': files[89:109],
}

print(files_dict)

dataset = {
    'train': list(),
    'val': list(),
    'test': list(),
}



selected_channels = ['Fc5.', 'Fc3.', 'Fc1.', 'Fcz.', 'Fc2.', 'Fc4.', 'Fc6.', 'C5..', 'C3..', 'C1..', 'Cz..', 'C2..',
                     'C4..', 'C6..', 'Cp5.', 'Cp3.', 'Cp1.', 'Cpz.', 'Cp2.', 'Cp4.', 'Cp6.', 'Fp1.', 'Fpz.', 'Fp2.',
                     'Af7.', 'Af3.', 'Afz.', 'Af4.', 'Af8.', 'F7..', 'F5..', 'F3..', 'F1..', 'Fz..', 'F2..', 'F4..',
                     'F6..', 'F8..', 'Ft7.', 'Ft8.', 'T7..', 'T8..', 'T9..', 'T10.', 'Tp7.', 'Tp8.', 'P7..', 'P5..',
                     'P3..', 'P1..', 'Pz..', 'P2..', 'P4..', 'P6..', 'P8..', 'Po7.', 'Po3.', 'Poz.', 'Po4.', 'Po8.',
                     'O1..', 'Oz..', 'O2..', 'Iz..']

db = lmdb.open('/data/datasets/eeg-motor-movementimagery-dataset-1.0.0/processed_average', map_size=4614542346)

for files_key in files_dict.keys():
    for file in files_dict[files_key]:
        for task in tasks:
            raw = mne.io.read_raw_edf(os.path.join(root_dir, file, f'{file}R{task}.edf'), preload=True)
            raw.pick_channels(selected_channels, ordered=True)
            if len(raw.info['bads']) > 0:
                print('interpolate_bads')
                raw.interpolate_bads()
            raw.set_eeg_reference(ref_channels='average')
            raw.filter(l_freq=0.3, h_freq=None)
            raw.notch_filter((60))
            raw.resample(200)
            events_from_annot, event_dict = mne.events_from_annotations(raw)
            epochs = mne.Epochs(raw,
                                events_from_annot,
                                event_dict,
                                tmin=0,
                                tmax=4. - 1.0 / raw.info['sfreq'],
                                baseline=None,
                                preload=True)
            data = epochs.get_data(units='uV')
            events = epochs.events[:, 2]
            print(data.shape, events)
            data = data[:, :, -800:]
            bz, ch_nums, _ = data.shape
            data = data.reshape(bz, ch_nums, 4, 200)
            print(data.shape)
            for i, (sample, event) in enumerate(zip(data, events)):
                if event != 1:
                    sample_key = f'{file}R{task}-{i}'
                    data_dict = {
                        'sample': sample, 'label': event - 2 if task in ['04', '08', '12'] else event
                    }
                    txn = db.begin(write=True)
                    txn.put(key=sample_key.encode(), value=pickle.dumps(data_dict))
                    txn.commit()
                    dataset[files_key].append(sample_key)

txn = db.begin(write=True)
txn.put(key='__keys__'.encode(), value=pickle.dumps(dataset))
txn.commit()
db.close()