Python evoked.EvokedArray类代码示例

def test_evoked_arithmetic():
    """Test evoked arithmetic
    """
    ev = read_evokeds(fname, condition=0)
    ev1 = EvokedArray(np.ones_like(ev.data), ev.info, ev.times[0], nave=20)
    ev2 = EvokedArray(-np.ones_like(ev.data), ev.info, ev.times[0], nave=10)

    # combine_evoked([ev1, ev2]) should be the same as ev1 + ev2:
    # data should be added according to their `nave` weights
    # nave = ev1.nave + ev2.nave
    ev = ev1 + ev2
    assert_equal(ev.nave, ev1.nave + ev2.nave)
    assert_allclose(ev.data, 1. / 3. * np.ones_like(ev.data))
    ev = ev1 - ev2
    assert_equal(ev.nave, ev1.nave + ev2.nave)
    assert_equal(ev.comment, ev1.comment + ' - ' + ev2.comment)
    assert_allclose(ev.data, np.ones_like(ev1.data))
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        ev = merge_evoked([ev1, ev2])
    assert_true(len(w) >= 1)
    assert_allclose(ev.data, 1. / 3. * np.ones_like(ev.data))

    # default comment behavior if evoked.comment is None
    old_comment1 = ev1.comment
    old_comment2 = ev2.comment
    ev1.comment = None
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        ev = ev1 - ev2
        assert_equal(ev.comment, 'unknown')
    ev1.comment = old_comment1
    ev2.comment = old_comment2

    # equal weighting
    ev = combine_evoked([ev1, ev2], weights='equal')
    assert_allclose(ev.data, np.zeros_like(ev1.data))

    # combine_evoked([ev1, ev2], weights=[1, 0]) should yield the same as ev1
    ev = combine_evoked([ev1, ev2], weights=[1, 0])
    assert_equal(ev.nave, ev1.nave)
    assert_allclose(ev.data, ev1.data)

    # simple subtraction (like in oddball)
    ev = combine_evoked([ev1, ev2], weights=[1, -1])
    assert_allclose(ev.data, 2 * np.ones_like(ev1.data))

    assert_raises(ValueError, combine_evoked, [ev1, ev2], weights='foo')
    assert_raises(ValueError, combine_evoked, [ev1, ev2], weights=[1])

    # grand average
    evoked1, evoked2 = read_evokeds(fname, condition=[0, 1], proj=True)
    ch_names = evoked1.ch_names[2:]
    evoked1.info['bads'] = ['EEG 008']  # test interpolation
    evoked1.drop_channels(evoked1.ch_names[:1])
    evoked2.drop_channels(evoked2.ch_names[1:2])
    gave = grand_average([evoked1, evoked2])
    assert_equal(gave.data.shape, [len(ch_names), evoked1.data.shape[1]])
    assert_equal(ch_names, gave.ch_names)
    assert_equal(gave.nave, 2)

def test_evoked_baseline():
    """Test evoked baseline."""
    evoked = read_evokeds(fname, condition=0, baseline=None)

    # Here we create a data_set with constant data.
    evoked = EvokedArray(np.ones_like(evoked.data), evoked.info,
                         evoked.times[0])

    # Mean baseline correction is applied, since the data is equal to its mean
    # the resulting data should be a matrix of zeroes.
    evoked.apply_baseline((None, None))

    assert_allclose(evoked.data, np.zeros_like(evoked.data))

def test_array_epochs():
    """Test creating evoked from array."""
    tempdir = _TempDir()

    # creating
    rng = np.random.RandomState(42)
    data1 = rng.randn(20, 60)
    sfreq = 1e3
    ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
    types = ['eeg'] * 20
    info = create_info(ch_names, sfreq, types)
    evoked1 = EvokedArray(data1, info, tmin=-0.01)

    # save, read, and compare evokeds
    tmp_fname = op.join(tempdir, 'evkdary-ave.fif')
    evoked1.save(tmp_fname)
    evoked2 = read_evokeds(tmp_fname)[0]
    data2 = evoked2.data
    assert_allclose(data1, data2)
    assert_allclose(evoked1.times, evoked2.times)
    assert_equal(evoked1.first, evoked2.first)
    assert_equal(evoked1.last, evoked2.last)
    assert_equal(evoked1.kind, evoked2.kind)
    assert_equal(evoked1.nave, evoked2.nave)

    # now compare with EpochsArray (with single epoch)
    data3 = data1[np.newaxis, :, :]
    events = np.c_[10, 0, 1]
    evoked3 = EpochsArray(data3, info, events=events, tmin=-0.01).average()
    assert_allclose(evoked1.data, evoked3.data)
    assert_allclose(evoked1.times, evoked3.times)
    assert_equal(evoked1.first, evoked3.first)
    assert_equal(evoked1.last, evoked3.last)
    assert_equal(evoked1.kind, evoked3.kind)
    assert_equal(evoked1.nave, evoked3.nave)

    # test kind check
    with pytest.raises(ValueError, match='Invalid value'):
        EvokedArray(data1, info, tmin=0, kind=1)
    with pytest.raises(ValueError, match='Invalid value'):
        EvokedArray(data1, info, kind='mean')

    # test match between channels info and data
    ch_names = ['EEG %03d' % (i + 1) for i in range(19)]
    types = ['eeg'] * 19
    info = create_info(ch_names, sfreq, types)
    pytest.raises(ValueError, EvokedArray, data1, info, tmin=-0.01)

def test_arithmetic():
    """Test evoked arithmetic."""
    ev = read_evokeds(fname, condition=0)
    ev1 = EvokedArray(np.ones_like(ev.data), ev.info, ev.times[0], nave=20)
    ev2 = EvokedArray(-np.ones_like(ev.data), ev.info, ev.times[0], nave=10)

    # combine_evoked([ev1, ev2]) should be the same as ev1 + ev2:
    # data should be added according to their `nave` weights
    # nave = ev1.nave + ev2.nave
    ev = combine_evoked([ev1, ev2], weights='nave')
    assert_equal(ev.nave, ev1.nave + ev2.nave)
    assert_allclose(ev.data, 1. / 3. * np.ones_like(ev.data))

    # with same trial counts, a bunch of things should be equivalent
    for weights in ('nave', 'equal', [0.5, 0.5]):
        ev = combine_evoked([ev1, ev1], weights=weights)
        assert_allclose(ev.data, ev1.data)
        assert_equal(ev.nave, 2 * ev1.nave)
        ev = combine_evoked([ev1, -ev1], weights=weights)
        assert_allclose(ev.data, 0., atol=1e-20)
        assert_equal(ev.nave, 2 * ev1.nave)
    ev = combine_evoked([ev1, -ev1], weights='equal')
    assert_allclose(ev.data, 0., atol=1e-20)
    assert_equal(ev.nave, 2 * ev1.nave)
    ev = combine_evoked([ev1, -ev2], weights='equal')
    expected = int(round(1. / (0.25 / ev1.nave + 0.25 / ev2.nave)))
    assert_equal(expected, 27)  # this is reasonable
    assert_equal(ev.nave, expected)

    # default comment behavior if evoked.comment is None
    old_comment1 = ev1.comment
    old_comment2 = ev2.comment
    ev1.comment = None
    ev = combine_evoked([ev1, -ev2], weights=[1, -1])
    assert_equal(ev.comment.count('unknown'), 2)
    assert_true('-unknown' in ev.comment)
    assert_true(' + ' in ev.comment)
    ev1.comment = old_comment1
    ev2.comment = old_comment2

    # equal weighting
    ev = combine_evoked([ev1, ev2], weights='equal')
    assert_allclose(ev.data, np.zeros_like(ev1.data))

    # combine_evoked([ev1, ev2], weights=[1, 0]) should yield the same as ev1
    ev = combine_evoked([ev1, ev2], weights=[1, 0])
    assert_equal(ev.nave, ev1.nave)
    assert_allclose(ev.data, ev1.data)

    # simple subtraction (like in oddball)
    ev = combine_evoked([ev1, ev2], weights=[1, -1])
    assert_allclose(ev.data, 2 * np.ones_like(ev1.data))

    assert_raises(ValueError, combine_evoked, [ev1, ev2], weights='foo')
    assert_raises(ValueError, combine_evoked, [ev1, ev2], weights=[1])

    # grand average
    evoked1, evoked2 = read_evokeds(fname, condition=[0, 1], proj=True)
    ch_names = evoked1.ch_names[2:]
    evoked1.info['bads'] = ['EEG 008']  # test interpolation
    evoked1.drop_channels(evoked1.ch_names[:1])
    evoked2.drop_channels(evoked2.ch_names[1:2])
    gave = grand_average([evoked1, evoked2])
    assert_equal(gave.data.shape, [len(ch_names), evoked1.data.shape[1]])
    assert_equal(ch_names, gave.ch_names)
    assert_equal(gave.nave, 2)
    assert_raises(ValueError, grand_average, [1, evoked1])

from mne.io.meas_info import create_info
print data.shape

sfreq = d['sfreq']
ch_names = layout.names
print ch_names
types = ['mag'] * len(ch_names)
info = create_info(ch_names, sfreq, types)
events = np.vstack(zip([125 for _ in xrange(len(labels))], [0 for _ in xrange(len(labels))], labels))
print events.shape

#help(EpochsArray)
ea = EpochsArray(data, info, events, tmin=d['tmin'])

from mne.evoked import EvokedArray
evoked = EvokedArray(data[labels==1].mean(axis=0), info, tmin=d['tmin'])
print evoked
print info
times = np.arange(0.05, 0.15, 0.01)
print len(info['ch_names'])
evoked.plot_topomap(0.15, ch_type='mag', layout=layout, size=10, colorbar=False)
#evoked.plot_topomap(times, ch_type='mag', layout=layout, size=10, colorbar=False)

evoked = EvokedArray(data[labels==0].mean(axis=0), info, tmin=d['tmin'])
evoked.plot_topomap(0.15, ch_type='mag', layout=layout, size=10, colorbar=False)
#evoked.plot_topomap(times, ch_type='mag', layout=layout, size=10, colorbar=False)

evoked = EvokedArray(data[labels==1].mean(axis=0) - data[labels==0].mean(axis=0), info, tmin=d['tmin'])
evoked.plot_topomap(0.25, ch_type='mag', layout=layout, size=10, colorbar=False)