from mne import create_info
from mne.io import RawArray
from mne.channels import make_standard_montage
import numpy as np

try:
    from ...moabb.datasets.base import BaseDataset
except:
    from moabb.datasets.base import BaseDataset

class FakeDataset(BaseDataset):
    """Fake Dataset for test purpose.

    The dataset has 2 sessions, 10 subjects, and 3 classes.

    """

    def __init__(self, event_list=('fake_c1', 'fake_c2', 'fake_c3'),
                 n_sessions=2, n_runs=2, n_subjects=10, paradigm='imagery'):
        self.n_runs = n_runs
        event_id = {ev: ii + 1 for ii, ev in enumerate(event_list)}
        super().__init__(list(range(1, n_subjects + 1)), n_sessions, event_id,
                         'FakeDataset', [0, 3], paradigm)

    def _get_single_subject_data(self, subject):

        data = dict()
        for session in range(self.n_sessions):
            data[f"session_{session}"] = {f"run_{ii}": self._generate_raw()
                                          for ii in range(self.n_runs)}
        return data

    def _generate_raw(self):

        ch_names = ['C3', 'Cz', 'C4']

        montage = make_standard_montage('standard_1005')
        sfreq = 128
        duration = len(self.event_id) * 60
        eeg_data = 2e-5 * np.random.randn(duration * sfreq, len(ch_names))
        y = np.zeros((duration * sfreq))
        for ii, ev in enumerate(self.event_id):
            start_idx = ((1 + 5 * ii) * 128)
            jump = (5 * len(self.event_id) * 128)
            y[start_idx::jump] = self.event_id[ev]

        ch_types = ['eeg'] * len(ch_names) + ['stim']
        ch_names = ch_names + ['stim']

        eeg_data = np.c_[eeg_data, y]

        info = create_info(ch_names=ch_names, ch_types=ch_types,
                           sfreq=sfreq, montage=montage)
        raw = RawArray(data=eeg_data.T, info=info, verbose=False)
        return raw

    def data_path(self, subject, path=None, force_update=False,
                  update_path=None, verbose=None):
        pass
