def _get_raw(tmpdir_factory, description=None): _, fnames = create_mne_dummy_raw( 2, 20000, 100, description=description, savedir=tmpdir_factory.mktemp('data'), save_format='fif', random_state=87) raw = mne.io.read_raw_fif(fnames['fif'], preload=False, verbose=None) return raw
def test_create_mne_dummy_raw(tmp_path): n_channels, n_times, sfreq = 2, 10000, 100 raw, fnames = create_mne_dummy_raw( n_channels, n_times, sfreq, savedir=tmp_path, save_format=['fif', 'hdf5']) assert isinstance(raw, mne.io.RawArray) assert len(raw.ch_names) == n_channels assert raw.n_times == n_times assert raw.info['sfreq'] == sfreq assert isinstance(fnames, dict) assert os.path.isfile(fnames['fif']) assert os.path.isfile(fnames['hdf5']) raw = mne.io.read_raw_fif(fnames['fif'], preload=False, verbose=None) with h5py.File(fnames['hdf5']) as hf: _ = np.array(hf['fake_raw'])
def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq, fake_duration_s): datasets = [] for i in range(n_fake_recs): train_or_eval = "eval" if i == 0 else "train" raw, save_fname = create_mne_dummy_raw( n_channels=n_fake_chs, n_times=fake_duration_s*fake_sfreq, sfreq=fake_sfreq, savedir=None) target = np.random.randint(0, 100, n_classes) if n_classes == 1: target = target[0] fake_descrition = pd.Series( data=[target, train_or_eval], index=["target", "session"]) base_ds = BaseDataset(raw, fake_descrition, target_name="target") datasets.append(base_ds) dataset = BaseConcatDataset(datasets) return dataset