Python Epochs.equalize_event_counts方法代码示例

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import equalize_event_counts [as 别名]
def test_ems():
    """Test event-matched spatial filters"""
    raw = io.Raw(raw_fname, preload=False)

    # create unequal number of events
    events = read_events(event_name)
    events[-2, 2] = 3
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    assert_raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
    epochs.equalize_event_counts(epochs.event_id, copy=False)

    assert_raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
    surrogates, filters, conditions = compute_ems(epochs)
    assert_equal(list(set(conditions)), [1, 3])

    events = read_events(event_name)
    event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
    epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs.equalize_event_counts(epochs.event_id, copy=False)

    n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])

    assert_raises(ValueError, compute_ems, epochs)
    surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
    assert_equal(n_expected, len(surrogates))
    assert_equal(n_expected, len(conditions))
    assert_equal(list(set(conditions)), [2, 3])
    raw.close()

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import equalize_event_counts [as 别名]
def test_epoch_eq():
    """Test epoch count equalization and condition combining
    """
    # equalizing epochs objects
    epochs_1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_2 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    assert_true(epochs_1.events.shape[0] != epochs_2.events.shape[0])
    equalize_epoch_counts([epochs_1, epochs_2], method='mintime')
    assert_true(epochs_1.events.shape[0] == epochs_2.events.shape[0])
    epochs_3 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_4 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    equalize_epoch_counts([epochs_3, epochs_4], method='truncate')
    assert_true(epochs_1.events.shape[0] == epochs_3.events.shape[0])
    assert_true(epochs_3.events.shape[0] == epochs_4.events.shape[0])

    # equalizing conditions
    epochs = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3, 'd': 4},
                    tmin, tmax, picks=picks)
    old_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    epochs.equalize_event_counts(['a', 'b'], copy=False)
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_true(new_shapes[0] == new_shapes[1])
    assert_true(new_shapes[2] == new_shapes[2])
    assert_true(new_shapes[3] == new_shapes[3])
    # now with two conditions collapsed
    old_shapes = new_shapes
    epochs.equalize_event_counts([['a', 'b'], 'c'], copy=False)
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2])
    assert_true(new_shapes[3] == old_shapes[3])
    assert_raises(KeyError, epochs.equalize_event_counts, [1, 'a'])

    # now let's combine conditions
    old_shapes = new_shapes
    epochs = epochs.equalize_event_counts([['a', 'b'], ['c', 'd']])[0]
    new_shapes = [epochs[key].events.shape[0] for key in ['a', 'b', 'c', 'd']]
    assert_true(old_shapes[0] + old_shapes[1] == new_shapes[0] + new_shapes[1])
    assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2] + new_shapes[3])
    assert_raises(ValueError, combine_event_ids, epochs, ['a', 'b'],
                  {'ab': 1})

    combine_event_ids(epochs, ['a', 'b'], {'ab': 12}, copy=False)
    caught = 0
    for key in ['a', 'b']:
        try:
            epochs[key]
        except KeyError:
            caught += 1
    assert_raises(caught == 2)
    assert_true(not np.any(epochs.events[:, 2] == 1))
    assert_true(not np.any(epochs.events[:, 2] == 2))
    epochs = combine_event_ids(epochs, ['c', 'd'], {'cd': 34})
    assert_true(np.all(np.logical_or(epochs.events[:, 2] == 12,
                                     epochs.events[:, 2] == 34)))
    assert_true(epochs['ab'].events.shape[0] == old_shapes[0] + old_shapes[1])
    assert_true(epochs['ab'].events.shape[0] == epochs['cd'].events.shape[0])

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import equalize_event_counts [as 别名]
def test_epoch_eq():
    """Test epoch count equalization and condition combining
    """
    # equalizing epochs objects
    epochs_1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_2 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    epochs_1.drop_bad_epochs()  # make sure drops are logged
    assert_true(len([l for l in epochs_1.drop_log if not l]) == len(epochs_1.events))
    drop_log1 = epochs_1.drop_log = [[] for _ in range(len(epochs_1.events))]
    drop_log2 = [[] if l == ["EQUALIZED_COUNT"] else l for l in epochs_1.drop_log]
    assert_true(drop_log1 == drop_log2)
    assert_true(len([l for l in epochs_1.drop_log if not l]) == len(epochs_1.events))
    assert_true(epochs_1.events.shape[0] != epochs_2.events.shape[0])
    equalize_epoch_counts([epochs_1, epochs_2], method="mintime")
    assert_true(epochs_1.events.shape[0] == epochs_2.events.shape[0])
    epochs_3 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
    epochs_4 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
    equalize_epoch_counts([epochs_3, epochs_4], method="truncate")
    assert_true(epochs_1.events.shape[0] == epochs_3.events.shape[0])
    assert_true(epochs_3.events.shape[0] == epochs_4.events.shape[0])

    # equalizing conditions
    epochs = Epochs(raw, events, {"a": 1, "b": 2, "c": 3, "d": 4}, tmin, tmax, picks=picks, reject=reject)
    epochs.drop_bad_epochs()  # make sure drops are logged
    assert_true(len([l for l in epochs.drop_log if not l]) == len(epochs.events))
    drop_log1 = deepcopy(epochs.drop_log)
    old_shapes = [epochs[key].events.shape[0] for key in ["a", "b", "c", "d"]]
    epochs.equalize_event_counts(["a", "b"], copy=False)
    # undo the eq logging
    drop_log2 = [[] if l == ["EQUALIZED_COUNT"] else l for l in epochs.drop_log]
    assert_true(drop_log1 == drop_log2)

    assert_true(len([l for l in epochs.drop_log if not l]) == len(epochs.events))
    new_shapes = [epochs[key].events.shape[0] for key in ["a", "b", "c", "d"]]
    assert_true(new_shapes[0] == new_shapes[1])
    assert_true(new_shapes[2] == new_shapes[2])
    assert_true(new_shapes[3] == new_shapes[3])
    # now with two conditions collapsed
    old_shapes = new_shapes
    epochs.equalize_event_counts([["a", "b"], "c"], copy=False)
    new_shapes = [epochs[key].events.shape[0] for key in ["a", "b", "c", "d"]]
    assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2])
    assert_true(new_shapes[3] == old_shapes[3])
    assert_raises(KeyError, epochs.equalize_event_counts, [1, "a"])

    # now let's combine conditions
    old_shapes = new_shapes
    epochs = epochs.equalize_event_counts([["a", "b"], ["c", "d"]])[0]
    new_shapes = [epochs[key].events.shape[0] for key in ["a", "b", "c", "d"]]
    assert_true(old_shapes[0] + old_shapes[1] == new_shapes[0] + new_shapes[1])
    assert_true(new_shapes[0] + new_shapes[1] == new_shapes[2] + new_shapes[3])
    assert_raises(ValueError, combine_event_ids, epochs, ["a", "b"], {"ab": 1})

    combine_event_ids(epochs, ["a", "b"], {"ab": 12}, copy=False)
    caught = 0
    for key in ["a", "b"]:
        try:
            epochs[key]
        except KeyError:
            caught += 1
    assert_raises(Exception, caught == 2)
    assert_true(not np.any(epochs.events[:, 2] == 1))
    assert_true(not np.any(epochs.events[:, 2] == 2))
    epochs = combine_event_ids(epochs, ["c", "d"], {"cd": 34})
    assert_true(np.all(np.logical_or(epochs.events[:, 2] == 12, epochs.events[:, 2] == 34)))
    assert_true(epochs["ab"].events.shape[0] == old_shapes[0] + old_shapes[1])
    assert_true(epochs["ab"].events.shape[0] == epochs["cd"].events.shape[0])

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import equalize_event_counts [as 别名]
def test_ems():
    """Test event-matched spatial filters"""
    raw = io.read_raw_fif(raw_fname, preload=False, add_eeg_ref=False)

    # create unequal number of events
    events = read_events(event_name)
    events[-2, 2] = 3
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True, add_eeg_ref=False)
    assert_raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
    epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]

    assert_raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
    surrogates, filters, conditions = compute_ems(epochs)
    assert_equal(list(set(conditions)), [1, 3])

    events = read_events(event_name)
    event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
    epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True, add_eeg_ref=False)
    epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]

    n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])

    assert_raises(ValueError, compute_ems, epochs)
    surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
    assert_equal(n_expected, len(surrogates))
    assert_equal(n_expected, len(conditions))
    assert_equal(list(set(conditions)), [2, 3])

    # test compute_ems cv
    epochs = epochs['aud_r', 'vis_l']
    epochs.equalize_event_counts(epochs.event_id)
    if check_version('sklearn', '0.18'):
        from sklearn.model_selection import StratifiedKFold
        cv = StratifiedKFold()
    else:
        from sklearn.cross_validation import StratifiedKFold
        cv = StratifiedKFold(epochs.events[:, 2])
    compute_ems(epochs, cv=cv)
    compute_ems(epochs, cv=2)
    assert_raises(ValueError, compute_ems, epochs, cv='foo')
    assert_raises(ValueError, compute_ems, epochs, cv=len(epochs) + 1)
    raw.close()

    # EMS transformer, check that identical to compute_ems
    X = epochs.get_data()
    y = epochs.events[:, 2]
    X = X / np.std(X)  # X scaled outside cv in compute_ems
    Xt, coefs = list(), list()
    ems = EMS()
    assert_equal(ems.__repr__(), '<EMS: not fitted.>')
    # manual leave-one-out to avoid sklearn version problem
    for test in range(len(y)):
        train = np.setdiff1d(range(len(y)), test)
        ems.fit(X[train], y[train])
        coefs.append(ems.filters_)
        Xt.append(ems.transform(X[[test]]))
    assert_equal(ems.__repr__(), '<EMS: fitted with 4 filters on 2 classes.>')
    assert_array_almost_equal(filters, np.mean(coefs, axis=0))
    assert_array_almost_equal(surrogates, np.vstack(Xt))