Python Epochs.pick_types Examples

Example #1

File: test_utils.py Project: jhouck/mne-python

def test_auto_scale():
    """Test auto-scaling of channels for quick plotting."""
    raw = read_raw_fif(raw_fname)
    epochs = Epochs(raw, read_events(ev_fname))
    rand_data = np.random.randn(10, 100)

    for inst in [raw, epochs]:
        scale_grad = 1e10
        scalings_def = dict([('eeg', 'auto'), ('grad', scale_grad),
                             ('stim', 'auto')])

        # Test for wrong inputs
        pytest.raises(ValueError, inst.plot, scalings='foo')
        pytest.raises(ValueError, _compute_scalings, 'foo', inst)

        # Make sure compute_scalings doesn't change anything not auto
        scalings_new = _compute_scalings(scalings_def, inst)
        assert (scale_grad == scalings_new['grad'])
        assert (scalings_new['eeg'] != 'auto')

    pytest.raises(ValueError, _compute_scalings, scalings_def, rand_data)
    epochs = epochs[0].load_data()
    epochs.pick_types(eeg=True, meg=False)
    pytest.raises(ValueError, _compute_scalings,
                  dict(grad='auto'), epochs)

Example #2

File: test_utils.py Project: jmontoyam/mne-python

def test_auto_scale():
    """Test auto-scaling of channels for quick plotting."""
    raw = read_raw_fif(raw_fname, preload=False, add_eeg_ref=False)
    ev = read_events(ev_fname)
    epochs = Epochs(raw, ev, add_eeg_ref=False)
    rand_data = np.random.randn(10, 100)

    for inst in [raw, epochs]:
        scale_grad = 1e10
        scalings_def = dict([('eeg', 'auto'), ('grad', scale_grad),
                             ('stim', 'auto')])

        # Test for wrong inputs
        assert_raises(ValueError, inst.plot, scalings='foo')
        assert_raises(ValueError, _compute_scalings, 'foo', inst)

        # Make sure compute_scalings doesn't change anything not auto
        scalings_new = _compute_scalings(scalings_def, inst)
        assert_true(scale_grad == scalings_new['grad'])
        assert_true(scalings_new['eeg'] != 'auto')

    assert_raises(ValueError, _compute_scalings, scalings_def, rand_data)
    epochs = epochs[0].load_data()
    epochs.pick_types(eeg=True, meg=False)
    assert_raises(ValueError, _compute_scalings, dict(grad='auto'), epochs)

Example #3

File: test_utils.py Project: yukids123/mne-python

def test_auto_scale():
    """Test auto-scaling of channels for quick plotting."""
    raw = read_raw_fif(raw_fname)
    epochs = Epochs(raw, read_events(ev_fname))
    rand_data = np.random.randn(10, 100)

    for inst in [raw, epochs]:
        scale_grad = 1e10
        scalings_def = dict([('eeg', 'auto'), ('grad', scale_grad),
                             ('stim', 'auto')])

        # Test for wrong inputs
        with pytest.raises(ValueError, match=r".*scalings.*'foo'.*"):
            inst.plot(scalings='foo')

        # Make sure compute_scalings doesn't change anything not auto
        scalings_new = _compute_scalings(scalings_def, inst)
        assert (scale_grad == scalings_new['grad'])
        assert (scalings_new['eeg'] != 'auto')

    with pytest.raises(ValueError, match='Must supply either Raw or Epochs'):
        _compute_scalings(scalings_def, rand_data)
    epochs = epochs[0].load_data()
    epochs.pick_types(eeg=True, meg=False)

Example #4

File: test_utils.py Project: mmagnuski/mne-python

def test_auto_scale():
    """Test auto-scaling of channels for quick plotting."""
    raw = read_raw_fif(raw_fname, preload=False)
    ev = read_events(ev_fname)
    epochs = Epochs(raw, ev)
    rand_data = np.random.randn(10, 100)

    for inst in [raw, epochs]:
        scale_grad = 1e10
        scalings_def = dict([("eeg", "auto"), ("grad", scale_grad), ("stim", "auto")])

        # Test for wrong inputs
        assert_raises(ValueError, inst.plot, scalings="foo")
        assert_raises(ValueError, _compute_scalings, "foo", inst)

        # Make sure compute_scalings doesn't change anything not auto
        scalings_new = _compute_scalings(scalings_def, inst)
        assert_true(scale_grad == scalings_new["grad"])
        assert_true(scalings_new["eeg"] != "auto")

    assert_raises(ValueError, _compute_scalings, scalings_def, rand_data)
    epochs = epochs[0].load_data()
    epochs.pick_types(eeg=True, meg=False)
    assert_raises(ValueError, _compute_scalings, dict(grad="auto"), epochs)