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)
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)
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)
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)