def test_strip_dev(version, want, have_unstripped, monkeypatch):
"""Test that stripping dev works."""
monkeypatch.setattr(mne.utils.check, 'import_module',
lambda x: Bunch(__version__=version))
got_have_unstripped, same_version = check_version(version,
want,
strip=False,
return_version=True)
assert same_version == version
assert got_have_unstripped is have_unstripped
have, simpler_version = check_version(
'foo', want, return_version=True) # strip=True is the default
assert have, (simpler_version, version)
def looks_stable(version):
try:
[int(x) for x in version.split('.')]
except ValueError:
return False
else:
return True
if looks_stable(version):
assert 'dev' not in version
assert 'rc' not in version
assert simpler_version == version
else:
assert simpler_version != version
assert 'dev' not in simpler_version
assert 'rc' not in simpler_version
assert not simpler_version.endswith('.')
assert looks_stable(simpler_version)
def test_scaler():
"""Test methods of Scaler."""
raw = io.read_raw_fif(raw_fname)
events = read_events(event_name)
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)
epochs_data = epochs.get_data()
y = epochs.events[:, -1]
methods = (None, dict(mag=5, grad=10, eeg=20), 'mean', 'median')
infos = (epochs.info, epochs.info, None, None)
epochs_data_t = epochs_data.transpose([1, 0, 2])
for method, info in zip(methods, infos):
if method == 'median' and not check_version('sklearn', '0.17'):
assert_raises(ValueError, Scaler, info, method)
continue
if method == 'mean' and not check_version('sklearn', ''):
assert_raises(ImportError, Scaler, info, method)
continue
scaler = Scaler(info, method)
X = scaler.fit_transform(epochs_data, y)
assert_equal(X.shape, epochs_data.shape)
if method is None or isinstance(method, dict):
sd = DEFAULTS['scalings'] if method is None else method
stds = np.zeros(len(picks))
for key in ('mag', 'grad'):
stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
means = np.zeros(len(epochs.ch_names))
elif method == 'mean':
stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
else: # median
percs = np.array([np.percentile(ch_data, [25, 50, 75])
for ch_data in epochs_data_t])
stds = percs[:, 2] - percs[:, 0]
means = percs[:, 1]
assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
epochs_data, rtol=1e-12, atol=1e-20, err_msg=method)
X2 = scaler.fit(epochs_data, y).transform(epochs_data)
assert_array_equal(X, X2)
# inverse_transform
Xi = scaler.inverse_transform(X)
assert_array_almost_equal(epochs_data, Xi)
# Test init exception
assert_raises(ValueError, Scaler, None, None)
assert_raises(ValueError, scaler.fit, epochs, y)
assert_raises(ValueError, scaler.transform, epochs)
epochs_bad = Epochs(raw, events, event_id, 0, 0.01,
picks=np.arange(len(raw.ch_names))) # non-data chs
scaler = Scaler(epochs_bad.info, None)
assert_raises(ValueError, scaler.fit, epochs_bad.get_data(), y)
def test_set(_bids_validate):
"""Test write_raw_bids conversion for EEGLAB data."""
# standalone .set file
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'EEGLAB')
# .set with associated .fdt
output_path = _TempDir()
data_path = op.join(testing.data_path(), 'EEGLAB')
raw_fname = op.join(data_path, 'test_raw.set')
raw = mne.io.read_raw_eeglab(raw_fname)
# embedded - test mne-version assertion
tmp_version = mne.__version__
mne.__version__ = '0.16'
with pytest.raises(ValueError, match='Your version of MNE is too old.'):
write_raw_bids(raw, bids_basename, output_path)
mne.__version__ = tmp_version
# proceed with the actual test for EEGLAB data
write_raw_bids(raw, bids_basename, output_path, overwrite=False)
read_raw_bids(bids_basename + '_eeg.set', output_path)
with pytest.raises(TypeError, match="unexpected keyword argument 'foo'"):
read_raw_bids(bids_basename + '_eeg.set',
output_path,
extra_params=dict(foo='bar'))
with pytest.raises(FileExistsError, match="already exists"): # noqa: F821
write_raw_bids(raw,
bids_basename,
output_path=output_path,
overwrite=False)
_bids_validate(output_path)
# check events.tsv is written
# XXX: only from 0.18 onwards because events_from_annotations
# is broken for earlier versions
events_tsv_fname = op.join(output_path, 'sub-' + subject_id,
'ses-' + session_id, 'eeg',
bids_basename + '_events.tsv')
if check_version('mne', '0.18'):
assert op.exists(events_tsv_fname)
# Also cover iEEG
# We use the same data and pretend that eeg channels are ecog
raw.set_channel_types(
{raw.ch_names[i]: 'ecog'
for i in mne.pick_types(raw.info, eeg=True)})
output_path = _TempDir()
write_raw_bids(raw, bids_basename, output_path)
_bids_validate(output_path)
# test anonymize and convert
if check_version('mne', '0.20') and check_version('pybv', '0.2.0'):
output_path = _test_anonymize(raw, bids_basename)
_bids_validate(output_path)
def test_surface_source_morph_round_trip(smooth, lower, upper, n_warn, dtype):
"""Test round-trip morphing yields similar STCs."""
kwargs = dict(smooth=smooth, warn=True, subjects_dir=subjects_dir)
stc = mne.read_source_estimate(fname_smorph)
if dtype is complex:
stc.data = 1j * stc.data
assert_array_equal(stc.data.real, 0.)
if smooth == 'nearest' and not check_version('scipy', '1.3'):
with pytest.raises(ValueError, match='required to use nearest'):
morph = compute_source_morph(stc, 'sample', 'fsaverage', **kwargs)
return
with pytest.warns(None) as w:
morph = compute_source_morph(stc, 'sample', 'fsaverage', **kwargs)
w = [ww for ww in w if 'vertices not included' in str(ww.message)]
assert len(w) == n_warn
assert morph.morph_mat.shape == (20484, len(stc.data))
stc_fs = morph.apply(stc)
morph_back = compute_source_morph(
stc_fs, 'fsaverage', 'sample', spacing=stc.vertices, **kwargs)
assert morph_back.morph_mat.shape == (len(stc.data), 20484)
stc_back = morph_back.apply(stc_fs)
corr = np.corrcoef(stc.data.ravel(), stc_back.data.ravel())[0, 1]
assert lower <= corr <= upper
# check the round-trip power
assert_power_preserved(stc, stc_back)
def test_plot_topomap_cnorm():
"""Test colormap normalization."""
if check_version("matplotlib", "3.2.0"):
from matplotlib.colors import TwoSlopeNorm
else:
from matplotlib.colors import DivergingNorm as TwoSlopeNorm
np.random.seed(42)
v = np.random.uniform(low=-1, high=2.5, size=64)
v[:3] = [-1, 0, 2.5]
montage = make_standard_montage("biosemi64")
info = create_info(montage.ch_names, 256, "eeg").set_montage("biosemi64")
cnorm = TwoSlopeNorm(vmin=-1, vcenter=0, vmax=2.5)
# pass only cnorm, no vmin/vmax
plot_topomap(v, info, cnorm=cnorm)
# pass cnorm and vmin
msg = "vmin=-1.* is implicitly defined by cnorm, ignoring vmin=-10.*"
with pytest.warns(RuntimeWarning, match=msg):
plot_topomap(v, info, vmin=-10, cnorm=cnorm)
# pass cnorm and vmax
msg = "vmax=2.5 is implicitly defined by cnorm, ignoring vmax=10.*"
with pytest.warns(RuntimeWarning, match=msg):
plot_topomap(v, info, vmax=10, cnorm=cnorm)
def test_plot_head_positions():
"""Test plotting of head positions."""
import matplotlib.pyplot as plt
info = read_info(evoked_fname)
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
destination = (0., 0., 0.04)
with warnings.catch_warnings(record=True): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos,
mode='field',
info=info,
destination=destination)
else:
assert_raises(RuntimeError,
plot_head_positions,
pos,
mode='field',
info=info,
destination=destination)
plot_head_positions([pos, pos]) # list support
assert_raises(ValueError, plot_head_positions, ['pos'])
assert_raises(ValueError, plot_head_positions, pos[:, :9])
assert_raises(ValueError, plot_head_positions, pos, 'foo')
plt.close('all')
def test_plot_head_positions():
"""Test plotting of head positions."""
info = read_info(evoked_fname)
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
destination = (0., 0., 0.04)
with pytest.warns(None): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos,
mode='field',
info=info,
destination=destination)
else:
pytest.raises(RuntimeError,
plot_head_positions,
pos,
mode='field',
info=info,
destination=destination)
plot_head_positions([pos, pos]) # list support
pytest.raises(ValueError, plot_head_positions, ['pos'])
pytest.raises(ValueError, plot_head_positions, pos[:, :9])
pytest.raises(ValueError, plot_head_positions, pos, 'foo')
with pytest.raises(ValueError, match='shape'):
with pytest.warns(None): # old mpl no viridis warning
plot_head_positions(pos, axes=1.)
plt.close('all')
def test_cache_dir(tmpdir, numba_conditional):
"""Test use of cache dir."""
tempdir = str(tmpdir)
orig_dir = os.getenv('MNE_CACHE_DIR', None)
orig_size = os.getenv('MNE_MEMMAP_MIN_SIZE', None)
rng = np.random.RandomState(0)
X = rng.randn(9, 2, 10)
try:
os.environ['MNE_MEMMAP_MIN_SIZE'] = '1K'
os.environ['MNE_CACHE_DIR'] = tempdir
# Fix error for #1507: in-place when memmapping
with catch_logging() as log_file:
permutation_cluster_1samp_test(
X, buffer_size=None, n_jobs=2, n_permutations=1,
seed=0, stat_fun=ttest_1samp_no_p, verbose=False)
assert 'independently' not in log_file.getvalue()
# ensure that non-independence yields warning
stat_fun = partial(ttest_1samp_no_p, sigma=1e-3)
if check_version('numpy', '1.17'):
random_state = np.random.default_rng(0)
else:
random_state = 0
with pytest.warns(RuntimeWarning, match='independently'):
permutation_cluster_1samp_test(
X, buffer_size=10, n_jobs=2, n_permutations=1,
seed=random_state, stat_fun=stat_fun, verbose=False)
finally:
if orig_dir is not None:
os.environ['MNE_CACHE_DIR'] = orig_dir
else:
del os.environ['MNE_CACHE_DIR']
if orig_size is not None:
os.environ['MNE_MEMMAP_MIN_SIZE'] = orig_size
else:
del os.environ['MNE_MEMMAP_MIN_SIZE']
def test_add_patch_info(monkeypatch):
"""Test adding patch info to source space."""
# let's setup a small source space
src = _read_small_src(remove=False)
src_new = _read_small_src()
# test that no patch info is added for small dist_limit
add_source_space_distances(src_new, dist_limit=0.00001)
assert all(s['nearest'] is None for s in src_new)
assert all(s['nearest_dist'] is None for s in src_new)
assert all(s['pinfo'] is None for s in src_new)
# now let's use one that works (and test our warning-throwing)
with monkeypatch.context() as m:
m.setattr(mne.source_space, '_DIST_WARN_LIMIT', 1)
with pytest.warns(RuntimeWarning, match='Computing distances for 258'):
add_source_space_distances(src_new)
_compare_source_spaces(src, src_new, 'approx')
# Old SciPy can't do patch info only
src_new = _read_small_src()
with monkeypatch.context() as m:
m.setattr(scipy, '__version__', '1.0')
with pytest.raises(RuntimeError, match='required to calculate patch '):
add_source_space_distances(src_new, dist_limit=0)
# New SciPy can
if check_version('scipy', '1.3'):
src_nodist = src.copy()
for s in src_nodist:
for key in ('dist', 'dist_limit'):
s[key] = None
add_source_space_distances(src_new, dist_limit=0)
_compare_source_spaces(src, src_new, 'approx')
def test_read_raw_curry(fname, tol, preload, bdf_curry_ref):
"""Test reading CURRY files."""
with pytest.warns(None) as wrn:
raw = read_raw_curry(fname, preload=preload)
if not check_version('numpy',
'1.16') and preload and fname.endswith('ASCII.dat'):
assert len(wrn) > 0
else:
assert len(wrn) == 0
assert hasattr(raw, '_data') == preload
assert raw.n_times == bdf_curry_ref.n_times
assert raw.info['sfreq'] == bdf_curry_ref.info['sfreq']
for field in ['kind', 'ch_name']:
assert_array_equal([ch[field] for ch in raw.info['chs']],
[ch[field] for ch in bdf_curry_ref.info['chs']])
raw.verbose = 'error' # don't emit warnings about slow reading
assert_allclose(raw.get_data(), bdf_curry_ref.get_data(), atol=tol)
picks, start, stop = ["C3", "C4"], 200, 800
assert_allclose(raw.get_data(picks=picks, start=start, stop=stop),
bdf_curry_ref.get_data(picks=picks, start=start,
stop=stop),
rtol=tol)
assert raw.info['dev_head_t'] is None
def test_bti(_bids_validate):
"""Test functionality of the write_raw_bids conversion for BTi data."""
bids_root = _TempDir()
data_path = op.join(base_path, 'bti', 'tests', 'data')
raw_fname = op.join(data_path, 'test_pdf_linux')
config_fname = op.join(data_path, 'test_config_linux')
headshape_fname = op.join(data_path, 'test_hs_linux')
raw = mne.io.read_raw_bti(raw_fname, config_fname=config_fname,
head_shape_fname=headshape_fname)
# write the BIDS dataset description, then write BIDS files
make_dataset_description(bids_root, name="BTi data")
write_raw_bids(raw, bids_basename, bids_root, verbose=True)
assert op.exists(op.join(bids_root, 'participants.tsv'))
_bids_validate(bids_root)
raw = read_raw_bids(bids_basename + '_meg', bids_root)
with pytest.raises(TypeError, match="unexpected keyword argument 'foo'"):
read_raw_bids(bids_basename + '_meg', bids_root,
extra_params=dict(foo='bar'))
if check_version('mne', '0.20'):
# test anonymize
raw = mne.io.read_raw_bti(raw_fname, config_fname=config_fname,
head_shape_fname=headshape_fname)
with pytest.warns(UserWarning,
match='Converting to FIF for anonymization'):
output_path = _test_anonymize(raw, bids_basename)
_bids_validate(output_path)
def test_check(tmpdir):
"""Test checking functions."""
pytest.raises(ValueError, check_random_state, 'foo')
pytest.raises(TypeError, _check_fname, 1)
_check_fname(Path('./'))
fname = str(tmpdir.join('foo'))
with open(fname, 'wb'):
pass
assert op.isfile(fname)
_check_fname(fname, overwrite='read', must_exist=True)
orig_perms = os.stat(fname).st_mode
os.chmod(fname, 0)
if not sys.platform.startswith('win'):
with pytest.raises(PermissionError, match='read permissions'):
_check_fname(fname, overwrite='read', must_exist=True)
os.chmod(fname, orig_perms)
os.remove(fname)
assert not op.isfile(fname)
pytest.raises(IOError, check_fname, 'foo', 'tets-dip.x', (), ('.fif', ))
pytest.raises(ValueError, _check_subject, None, None)
pytest.raises(TypeError, _check_subject, None, 1)
pytest.raises(TypeError, _check_subject, 1, None)
# smoke tests for permitted types
check_random_state(None).choice(1)
check_random_state(0).choice(1)
check_random_state(np.random.RandomState(0)).choice(1)
if check_version('numpy', '1.17'):
check_random_state(np.random.default_rng(0)).choice(1)
# _meg.fif is a valid ending and should not raise an error
new_fname = str(
tmpdir.join(op.basename(fname_raw).replace('_raw.', '_meg.')))
shutil.copyfile(fname_raw, new_fname)
mne.io.read_raw_fif(new_fname)
def test_ctf(_bids_validate):
"""Test functionality of the write_raw_bids conversion for CTF data."""
bids_root = _TempDir()
data_path = op.join(testing.data_path(download=False), 'CTF')
raw_fname = op.join(data_path, 'testdata_ctf.ds')
raw = mne.io.read_raw_ctf(raw_fname)
with pytest.warns(UserWarning, match='No line frequency'):
write_raw_bids(raw, bids_basename, bids_root=bids_root)
_bids_validate(bids_root)
with pytest.warns(UserWarning, match='Did not find any events'):
raw = read_raw_bids(bids_basename + '_meg.ds', bids_root,
extra_params=dict(clean_names=False))
# test to check that running again with overwrite == False raises an error
with pytest.raises(FileExistsError, match="already exists"): # noqa: F821
write_raw_bids(raw, bids_basename, bids_root=bids_root)
assert op.exists(op.join(bids_root, 'participants.tsv'))
# test anonymize
if check_version('mne', '0.20'):
raw = mne.io.read_raw_ctf(raw_fname)
with pytest.warns(UserWarning,
match='Converting to FIF for anonymization'):
output_path = _test_anonymize(raw, bids_basename)
_bids_validate(output_path)
raw.set_meas_date(None)
raw.anonymize()
with pytest.raises(ValueError, match='All measurement dates are None'):
get_anonymization_daysback(raw)
def test_check(tmp_path):
"""Test checking functions."""
pytest.raises(ValueError, check_random_state, 'foo')
pytest.raises(TypeError, _check_fname, 1)
_check_fname(Path('./foo'))
fname = tmp_path / 'foo'
with open(fname, 'wb'):
pass
assert op.isfile(fname)
_check_fname(fname, overwrite='read', must_exist=True)
orig_perms = os.stat(fname).st_mode
os.chmod(fname, 0)
if not sys.platform.startswith('win'):
with pytest.raises(PermissionError, match='read permissions'):
_check_fname(fname, overwrite='read', must_exist=True)
os.chmod(fname, orig_perms)
os.remove(fname)
assert not op.isfile(fname)
pytest.raises(IOError, check_fname, 'foo', 'tets-dip.x', (), ('.fif', ))
pytest.raises(ValueError, _check_subject, None, None)
pytest.raises(TypeError, _check_subject, None, 1)
pytest.raises(TypeError, _check_subject, 1, None)
# smoke tests for permitted types
check_random_state(None).choice(1)
check_random_state(0).choice(1)
check_random_state(np.random.RandomState(0)).choice(1)
if check_version('numpy', '1.17'):
check_random_state(np.random.default_rng(0)).choice(1)
def test_simulate_sparse_stc_single_hemi(_get_fwd_labels):
"""Test generation of sparse source estimate."""
fwd, labels = _get_fwd_labels
labels_single_hemi = labels[1:] # keep only labels in one hemisphere
n_times = 10
tmin = 0
tstep = 1e-3
times = np.arange(n_times, dtype=np.float64) * tstep + tmin
stc_1 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert (stc_1.data.shape[0] == len(labels_single_hemi))
assert (stc_1.data.shape[1] == n_times)
# make sure we get the same result when using the same seed
stc_2 = simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi, random_state=0)
assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
# smoke test for new API
if check_version('numpy', '1.17'):
simulate_sparse_stc(fwd['src'], len(labels_single_hemi), times,
labels=labels_single_hemi,
random_state=np.random.default_rng(0))
def _patch_setup_dots(mode, info, coils, ch):
"""Monkey patch _setup_dots for MNE-Python >= v0.24."""
from mne.forward._field_interpolation import _setup_dots
from mne.utils import check_version
if not check_version('mne', '0.24'):
return _setup_dots(mode, coils, ch)
else:
return _setup_dots(mode, info, coils, ch)
def _get_pca(rng=None):
if not check_version('sklearn', '0.18'):
from sklearn.decomposition import RandomizedPCA
return RandomizedPCA(n_components=2, whiten=True,
random_state=rng)
else:
from sklearn.decomposition import PCA
return PCA(n_components=2, whiten=True, svd_solver='randomized',
random_state=rng)
def _get_pca(rng=None):
if not check_version('sklearn', '0.18'):
from sklearn.decomposition import RandomizedPCA
return RandomizedPCA(n_components=2, whiten=True,
random_state=rng)
else:
from sklearn.decomposition import PCA
return PCA(n_components=2, whiten=True, svd_solver='randomized',
random_state=rng)
def test_decoding_time():
"""Test TimeDecoding."""
from sklearn.svm import SVR
if check_version('sklearn', '0.18'):
from sklearn.model_selection import KFold
else:
from sklearn.cross_validation import KFold
epochs = make_epochs()
with warnings.catch_warnings(record=True): # dep
tg = TimeDecoding()
assert_equal("<TimeDecoding | no fit, no prediction, no score>", '%s' % tg)
assert_true(hasattr(tg, 'times'))
assert_true(not hasattr(tg, 'train_times'))
assert_true(not hasattr(tg, 'test_times'))
tg.fit(epochs)
assert_equal(
"<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), no prediction, no score>", '%s' % tg)
assert_true(not hasattr(tg, 'train_times_'))
assert_true(not hasattr(tg, 'test_times_'))
assert_raises(RuntimeError, tg.score, epochs=None)
with warnings.catch_warnings(record=True): # not vectorizing
tg.predict(epochs)
assert_equal(
"<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), predicted 14 epochs, no score>", '%s' % tg)
assert_array_equal(np.shape(tg.y_pred_), [15, 14, 1])
with warnings.catch_warnings(record=True): # not vectorizing
tg.score(epochs)
tg.score()
assert_array_equal(np.shape(tg.scores_), [15])
assert_equal(
"<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), predicted 14 epochs,\n scored (accuracy_score)>", '%s' % tg)
# Test with regressor
clf = SVR()
cv = KFold(len(epochs))
y = np.random.rand(len(epochs))
with warnings.catch_warnings(record=True): # dep
tg = TimeDecoding(clf=clf, cv=cv)
tg.fit(epochs, y=y)
# Test scorer parameter to accept string
epochs.crop(epochs.times[0], epochs.times[2])
with warnings.catch_warnings(record=True): # dep
td_1 = TimeDecoding(scorer='accuracy')
td_1.fit(epochs)
score_1 = td_1.score(epochs)
with warnings.catch_warnings(record=True): # dep
td_2 = TimeDecoding()
td_2.fit(epochs)
score_2 = td_2.score(epochs)
assert_array_equal(score_1, score_2)
td_1.scorer = 'accuracies'
assert_raises(KeyError, td_1.score, epochs)
def test_bdf(_bids_validate):
"""Test write_raw_bids conversion for Biosemi data."""
bids_root = _TempDir()
data_path = op.join(base_path, 'edf', 'tests', 'data')
raw_fname = op.join(data_path, 'test.bdf')
raw = mne.io.read_raw_bdf(raw_fname)
with pytest.warns(UserWarning, match='No line frequency found'):
write_raw_bids(raw, bids_basename, bids_root, overwrite=False)
_bids_validate(bids_root)
# Test also the reading of channel types from channels.tsv
# the first channel in the raw data is not MISC right now
test_ch_idx = 0
assert coil_type(raw.info, test_ch_idx) != 'misc'
# we will change the channel type to MISC and overwrite the channels file
bids_fname = bids_basename + '_eeg.bdf'
channels_fname = _find_matching_sidecar(bids_fname, bids_root,
'channels.tsv')
channels_dict = _from_tsv(channels_fname)
channels_dict['type'][test_ch_idx] = 'MISC'
_to_tsv(channels_dict, channels_fname)
# Now read the raw data back from BIDS, with the tampered TSV, to show
# that the channels.tsv truly influences how read_raw_bids sets ch_types
# in the raw data object
raw = read_raw_bids(bids_fname, bids_root)
assert coil_type(raw.info, test_ch_idx) == 'misc'
with pytest.raises(TypeError, match="unexpected keyword argument 'foo'"):
read_raw_bids(bids_fname, bids_root, extra_params=dict(foo='bar'))
# Test cropped assertion error
raw = mne.io.read_raw_bdf(raw_fname)
raw.crop(0, raw.times[-2])
with pytest.raises(AssertionError, match='cropped'):
write_raw_bids(raw, bids_basename, bids_root)
# test anonymize and convert
if check_version('mne', '0.20') and check_version('pybv', '0.2.0'):
raw = mne.io.read_raw_bdf(raw_fname)
output_path = _test_anonymize(raw, bids_basename)
_bids_validate(output_path)
def test_decoding_time():
"""Test TimeDecoding."""
from sklearn.svm import SVR
if check_version('sklearn', '0.18'):
from sklearn.model_selection import KFold
else:
from sklearn.cross_validation import KFold
epochs = make_epochs()
with warnings.catch_warnings(record=True): # dep
tg = TimeDecoding()
assert_equal("<TimeDecoding | no fit, no prediction, no score>", '%s' % tg)
assert_true(hasattr(tg, 'times'))
assert_true(not hasattr(tg, 'train_times'))
assert_true(not hasattr(tg, 'test_times'))
tg.fit(epochs)
assert_equal("<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), no prediction, no score>", '%s' % tg)
assert_true(not hasattr(tg, 'train_times_'))
assert_true(not hasattr(tg, 'test_times_'))
assert_raises(RuntimeError, tg.score, epochs=None)
with warnings.catch_warnings(record=True): # not vectorizing
tg.predict(epochs)
assert_equal("<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), predicted 14 epochs, no score>",
'%s' % tg)
assert_array_equal(np.shape(tg.y_pred_), [15, 14, 1])
with warnings.catch_warnings(record=True): # not vectorizing
tg.score(epochs)
tg.score()
assert_array_equal(np.shape(tg.scores_), [15])
assert_equal("<TimeDecoding | fitted, start : -0.200 (s), stop : 0.499 "
"(s), predicted 14 epochs,\n scored (accuracy_score)>",
'%s' % tg)
# Test with regressor
clf = SVR()
cv = KFold(len(epochs))
y = np.random.rand(len(epochs))
with warnings.catch_warnings(record=True): # dep
tg = TimeDecoding(clf=clf, cv=cv)
tg.fit(epochs, y=y)
# Test scorer parameter to accept string
epochs.crop(epochs.times[0], epochs.times[2])
with warnings.catch_warnings(record=True): # dep
td_1 = TimeDecoding(scorer='accuracy')
td_1.fit(epochs)
score_1 = td_1.score(epochs)
with warnings.catch_warnings(record=True): # dep
td_2 = TimeDecoding()
td_2.fit(epochs)
score_2 = td_2.score(epochs)
assert_array_equal(score_1, score_2)
td_1.scorer = 'accuracies'
assert_raises(KeyError, td_1.score, epochs)
def test_inverse_coef():
"""Test inverse coefficients computation."""
from sklearn.linear_model import Ridge
rng = np.random.RandomState(0)
tmin, tmax = 0., 10.
n_feats, n_targets, n_samples = 64, 2, 10000
n_delays = int((tmax - tmin) + 1)
def make_data(n_feats, n_targets, n_samples, tmin, tmax):
X = rng.randn(n_samples, n_feats)
w = rng.randn(int((tmax - tmin) + 1) * n_feats, n_targets)
# Delay inputs
X_del = np.concatenate(_delay_time_series(X, tmin, tmax,
1.).transpose(2, 0, 1),
axis=1)
y = np.dot(X_del, w)
return X, y
# Check coefficient dims, for all estimator types
X, y = make_data(n_feats, n_targets, n_samples, tmin, tmax)
tdr = TimeDelayingRidge(tmin, tmax, 1., 0.1, 'laplacian')
for estimator in (0., 0.01, Ridge(alpha=0.), tdr):
rf = ReceptiveField(tmin, tmax, 1., estimator=estimator, patterns=True)
rf.fit(X, y)
inv_rf = ReceptiveField(tmin,
tmax,
1.,
estimator=estimator,
patterns=True)
inv_rf.fit(y, X)
assert_array_equal(rf.coef_.shape, rf.patterns_.shape,
(n_targets, n_feats, n_delays))
assert_array_equal(inv_rf.coef_.shape, inv_rf.patterns_.shape,
(n_feats, n_targets, n_delays))
# we should have np.dot(patterns.T,coef) ~ np.eye(n)
c0 = rf.coef_.reshape(n_targets, n_feats * n_delays)
c1 = rf.patterns_.reshape(n_targets, n_feats * n_delays)
assert_allclose(np.dot(c0, c1.T), np.eye(c0.shape[0]), atol=0.1)
# Check that warnings are issued when no regularization is applied
n_feats, n_targets, n_samples = 5, 60, 50
X, y = make_data(n_feats, n_targets, n_samples, tmin, tmax)
for estimator in (0., Ridge(alpha=0.)):
rf = ReceptiveField(tmin, tmax, 1., estimator=estimator, patterns=True)
with warnings.catch_warnings(record=True) as w:
rf.fit(y, X)
# For some reason there is no warning
if estimator and not check_version('numpy', '1.13'):
continue
assert_equal(len(w), 1)
assert any(x in str(w[0].message).lower()
for x in ('singular', 'scipy.linalg.solve'))
def _read_events(events_data, event_id, raw, ext, verbose=None):
"""Read in events data.
Parameters
----------
events_data : str | array | None
The events file. If a string, a path to the events file. If an array,
the MNE events array (shape n_events, 3). If None, events will be
inferred from the stim channel using `find_events`.
event_id : dict
The event id dict used to create a 'trial_type' column in events.tsv,
mapping a description key to an integer valued event code.
raw : instance of Raw
The data as MNE-Python Raw object.
ext : str
The extension of the original data file.
verbose : bool | str | int | None
If not None, override default verbose level (see :func:`mne.verbose`).
Returns
-------
events : array, shape = (n_events, 3)
The first column contains the event time in samples and the third
column contains the event id. The second column is ignored for now but
typically contains the value of the trigger channel either immediately
before the event or immediately after.
"""
if isinstance(events_data, str):
events = read_events(events_data, verbose=verbose).astype(int)
elif isinstance(events_data, np.ndarray):
if events_data.ndim != 2:
raise ValueError('Events must have two dimensions, '
'found %s' % events_data.ndim)
if events_data.shape[1] != 3:
raise ValueError('Events must have second dimension of length 3, '
'found %s' % events_data.shape[1])
events = events_data
elif 'stim' in raw:
events = find_events(raw,
min_duration=0.001,
initial_event=True,
verbose=verbose)
elif ext in ['.vhdr', '.set'] and check_version('mne', '0.18'):
events, event_id = events_from_annotations(raw,
event_id,
verbose=verbose)
else:
warn('No events found or provided. Please make sure to'
' set channel type using raw.set_channel_types'
' or provide events_data.')
events = None
return events, event_id
def test_xdawn_regularization():
"""Test Xdawn with regularization."""
# Get data, this time MEG so we can test proper reg/ch type support
raw = read_raw_fif(raw_fname, verbose=False, preload=True)
events = read_events(event_name)
picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
ecg=False, eog=False,
exclude='bads')[::8]
raw.pick_channels([raw.ch_names[pick] for pick in picks])
del picks
raw.info.normalize_proj()
epochs = Epochs(raw, events, event_id, tmin, tmax,
preload=True, baseline=None, verbose=False)
# Test with overlapping events.
# modify events to simulate one overlap
events = epochs.events
sel = np.where(events[:, 2] == 2)[0][:2]
modified_event = events[sel[0]]
modified_event[0] += 1
epochs.events[sel[1]] = modified_event
# Fit and check that overlap was found and applied
xd = Xdawn(n_components=2, correct_overlap='auto', reg='oas')
xd.fit(epochs)
assert xd.correct_overlap_
evoked = epochs['cond2'].average()
assert np.sum(np.abs(evoked.data - xd.evokeds_['cond2'].data))
# With covariance regularization
for reg in [.1, 0.1, 'ledoit_wolf', 'oas']:
xd = Xdawn(n_components=2, correct_overlap=False,
signal_cov=np.eye(len(epochs.ch_names)), reg=reg)
xd.fit(epochs)
# With bad shrinkage
xd = Xdawn(n_components=2, correct_overlap=False,
signal_cov=np.eye(len(epochs.ch_names)), reg=2)
with pytest.raises(ValueError, match='shrinkage must be'):
xd.fit(epochs)
# With rank-deficient input
# this is a bit wacky because `epochs` has projectors on from the old raw
# but it works as a rank-deficient test case
xd = Xdawn(correct_overlap=False, reg=0.5)
xd.fit(epochs)
xd = Xdawn(correct_overlap=False, reg='diagonal_fixed')
xd.fit(epochs)
bad_eig = (sys.platform.startswith('win') and
check_version('numpy', '1.16.5') and
'mkl_rt' in _get_numpy_libs()) # some problem with MKL on Win
if bad_eig:
pytest.skip('Unknown MKL+Windows error fails for eig check')
xd = Xdawn(correct_overlap=False, reg=None)
with pytest.raises(ValueError, match='Could not compute eigenvalues'):
xd.fit(epochs)
def test_inverse_coef():
"""Test inverse coefficients computation."""
from sklearn.linear_model import Ridge
rng = np.random.RandomState(0)
tmin, tmax = 0., 10.
n_feats, n_targets, n_samples = 64, 2, 10000
n_delays = int((tmax - tmin) + 1)
def make_data(n_feats, n_targets, n_samples, tmin, tmax):
X = rng.randn(n_samples, n_feats)
w = rng.randn(int((tmax - tmin) + 1) * n_feats, n_targets)
# Delay inputs
X_del = np.concatenate(
_delay_time_series(X, tmin, tmax, 1.).transpose(2, 0, 1), axis=1)
y = np.dot(X_del, w)
return X, y
# Check coefficient dims, for all estimator types
X, y = make_data(n_feats, n_targets, n_samples, tmin, tmax)
tdr = TimeDelayingRidge(tmin, tmax, 1., 0.1, 'laplacian')
for estimator in (0., 0.01, Ridge(alpha=0.), tdr):
rf = ReceptiveField(tmin, tmax, 1., estimator=estimator,
patterns=True)
rf.fit(X, y)
inv_rf = ReceptiveField(tmin, tmax, 1., estimator=estimator,
patterns=True)
inv_rf.fit(y, X)
assert_array_equal(rf.coef_.shape, rf.patterns_.shape,
(n_targets, n_feats, n_delays))
assert_array_equal(inv_rf.coef_.shape, inv_rf.patterns_.shape,
(n_feats, n_targets, n_delays))
# we should have np.dot(patterns.T,coef) ~ np.eye(n)
c0 = rf.coef_.reshape(n_targets, n_feats * n_delays)
c1 = rf.patterns_.reshape(n_targets, n_feats * n_delays)
assert_allclose(np.dot(c0, c1.T), np.eye(c0.shape[0]), atol=0.1)
# Check that warnings are issued when no regularization is applied
n_feats, n_targets, n_samples = 5, 60, 50
X, y = make_data(n_feats, n_targets, n_samples, tmin, tmax)
for estimator in (0., Ridge(alpha=0.)):
rf = ReceptiveField(tmin, tmax, 1., estimator=estimator, patterns=True)
with warnings.catch_warnings(record=True) as w:
rf.fit(y, X)
# For some reason there is no warning
if estimator and not check_version('numpy', '1.13'):
continue
assert_equal(len(w), 1)
assert_true(any(x in str(w[0].message).lower()
for x in ('singular', 'scipy.linalg.solve')),
msg=str(w[0].message))
def _read_raw(raw_path,
electrode=None,
hsp=None,
hpi=None,
allow_maxshield=False,
config_path=None,
**kwargs):
"""Read a raw file into MNE, making inferences based on extension."""
_, ext = _parse_ext(raw_path)
# KIT systems
if ext in ['.con', '.sqd']:
raw = io.read_raw_kit(raw_path,
elp=electrode,
hsp=hsp,
mrk=hpi,
preload=False,
**kwargs)
# BTi systems
elif ext == '.pdf':
raw = io.read_raw_bti(
pdf_fname=str(raw_path), # FIXME MNE should accept Path!
config_fname=str(config_path), # FIXME MNE should accept Path!
head_shape_fname=hsp,
preload=False,
**kwargs)
elif ext == '.fif':
raw = reader[ext](raw_path, allow_maxshield, **kwargs)
elif ext in ['.ds', '.vhdr', '.set', '.edf', '.bdf', '.EDF', '.snirf']:
if (ext == '.snirf'
and not check_version('mne', '1.0')): # pragma: no cover
raise RuntimeError(
'fNIRS support in MNE-BIDS requires MNE-Python version 1.0')
raw_path = Path(raw_path)
raw = reader[ext](raw_path, **kwargs)
# MEF and NWB are allowed, but not yet implemented
elif ext in ['.mef', '.nwb']:
raise ValueError(f'Got "{ext}" as extension. This is an allowed '
f'extension but there is no IO support for this '
f'file format yet.')
# No supported data found ...
# ---------------------------
else:
raise ValueError(f'Raw file name extension must be one '
f'of {ALLOWED_DATATYPE_EXTENSIONS}\n'
f'Got {ext}')
return raw
def test_plot_head_positions():
"""Test plotting of head positions."""
import matplotlib.pyplot as plt
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
with warnings.catch_warnings(record=True): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos, mode='field')
else:
assert_raises(RuntimeError, plot_head_positions, pos, mode='field')
assert_raises(ValueError, plot_head_positions, pos, 'foo')
plt.close('all')
def test_plot_head_positions():
"""Test plotting of head positions."""
import matplotlib.pyplot as plt
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
with warnings.catch_warnings(record=True): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos, mode='field')
else:
assert_raises(RuntimeError, plot_head_positions, pos, mode='field')
assert_raises(ValueError, plot_head_positions, pos, 'foo')
plt.close('all')
def _interpolate_bads_meg(inst, mode='accurate', origin=None, verbose=None):
"""Interpolate bad channels from data in good channels.
Parameters
----------
inst : mne.io.Raw, mne.Epochs or mne.Evoked
The data to interpolate. Must be preloaded.
mode : str
Either `'accurate'` or `'fast'`, determines the quality of the
Legendre polynomial expansion used for interpolation. `'fast'` should
be sufficient for most applications.
origin : None | list
If None, origin is set to sensor center of mass, otherwise use the
coordinates provided as origin. The old standard value is (0., 0., 0.04)
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
"""
from mne.channels.interpolation import _do_interp_dots
picks_meg = pick_types(inst.info, meg=True, eeg=False, exclude=[])
picks_good = pick_types(inst.info, meg=True, eeg=False, exclude='bads')
meg_ch_names = [inst.info['ch_names'][p] for p in picks_meg]
bads_meg = [ch for ch in inst.info['bads'] if ch in meg_ch_names]
# select the bad meg channel to be interpolated
if len(bads_meg) == 0:
picks_bad = []
else:
picks_bad = pick_channels(inst.info['ch_names'], bads_meg, exclude=[])
# return without doing anything if there are no meg channels
if len(picks_meg) == 0 or len(picks_bad) == 0:
return
info_from = pick_info(inst.info, picks_good)
info_to = pick_info(inst.info, picks_bad)
if check_version('mne', min_version='0.21'):
from mne.forward import _map_meg_or_eeg_channels
mapping = _map_meg_or_eeg_channels(info_from,
info_to,
mode=mode,
origin=origin)
else:
from mne.forward import _map_meg_channels
mapping = _map_meg_channels(info_from,
info_to,
mode=mode,
origin=origin)
_do_interp_dots(inst, mapping, picks_good, picks_bad)
def test_ci():
"""Test confidence intervals."""
# isolated test of CI functions
arr = np.linspace(0, 1, 1000)[..., np.newaxis]
assert_allclose(_ci(arr, method="parametric"),
_ci(arr, method="bootstrap"),
rtol=.005)
assert_allclose(_bootstrap_ci(arr, stat_fun="median", random_state=0),
_bootstrap_ci(arr, stat_fun="mean", random_state=0),
rtol=.1)
# smoke test for new API
if check_version('numpy', '1.17'):
random_state = np.random.default_rng(0)
_bootstrap_ci(arr, random_state=random_state)
def test_run_update_age_records(tmpdir):
"""Test Sleep Physionet URL handling."""
import pandas as pd
fname = op.join(str(tmpdir), "records.csv")
_update_sleep_age_records(fname)
data = pd.read_csv(fname)
if not check_version('pandas', '0.23.0'):
expected = pd.read_csv(AGE_SLEEP_RECORDS)
assert_array_equal(
data[['subject', 'sha', 'fname']].values,
expected[['subject', 'sha', 'fname']].values,
)
else:
pd.testing.assert_frame_equal(data, pd.read_csv(AGE_SLEEP_RECORDS))
def test_add_noise():
"""Test noise addition."""
if check_version('numpy', '1.17'):
rng = np.random.default_rng(0)
else:
rng = np.random.RandomState(0)
raw = read_raw_fif(raw_fname)
raw.del_proj()
picks = pick_types(raw.info, meg=True, eeg=True, exclude=())
cov = compute_raw_covariance(raw, picks=picks)
with pytest.raises(RuntimeError, match='to be loaded'):
add_noise(raw, cov)
raw.crop(0, 1).load_data()
with pytest.raises(TypeError, match='Raw, Epochs, or Evoked'):
add_noise(0., cov)
with pytest.raises(TypeError, match='Covariance'):
add_noise(raw, 0.)
# test a no-op (data preserved)
orig_data = raw[:][0]
zero_cov = cov.copy()
zero_cov['data'].fill(0)
add_noise(raw, zero_cov)
new_data = raw[:][0]
assert_allclose(orig_data, new_data, atol=1e-30)
# set to zero to make comparisons easier
raw._data[:] = 0.
epochs = EpochsArray(np.zeros((1, len(raw.ch_names), 100)),
raw.info.copy())
epochs.info['bads'] = []
evoked = epochs.average(picks=np.arange(len(raw.ch_names)))
for inst in (raw, epochs, evoked):
with catch_logging() as log:
add_noise(inst, cov, random_state=rng, verbose=True)
log = log.getvalue()
want = ('to {0}/{1} channels ({0}'
.format(len(cov['names']), len(raw.ch_names)))
assert want in log
if inst is evoked:
inst = EpochsArray(inst.data[np.newaxis], inst.info)
if inst is raw:
cov_new = compute_raw_covariance(inst, picks=picks)
else:
cov_new = compute_covariance(inst)
assert cov['names'] == cov_new['names']
r = np.corrcoef(cov['data'].ravel(), cov_new['data'].ravel())[0, 1]
assert r > 0.99
def test_check():
"""Test checking functions."""
pytest.raises(ValueError, check_random_state, 'foo')
pytest.raises(TypeError, _check_fname, 1)
pytest.raises(IOError, check_fname, 'foo', 'tets-dip.x', (), ('.fif', ))
pytest.raises(ValueError, _check_subject, None, None)
pytest.raises(TypeError, _check_subject, None, 1)
pytest.raises(TypeError, _check_subject, 1, None)
# smoke tests for permitted types
check_random_state(None).choice(1)
check_random_state(0).choice(1)
check_random_state(np.random.RandomState(0)).choice(1)
if check_version('numpy', '1.17'):
check_random_state(np.random.default_rng(0)).choice(1)
# _meg.fif is a valid ending and should not raise an error
sh.copyfile(fname_raw, fname_raw.replace('_raw.', '_meg.'))
mne.io.read_raw_fif(fname_raw.replace('_raw.', '_meg.'))
def test_find_emptyroom_ties():
"""Test that we receive a warning on a date tie."""
data_path = testing.data_path()
raw_fname = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
bids_root = _TempDir()
bids_path.update(root=bids_root)
session = '20010101'
er_dir_path = BIDSPath(subject='emptyroom',
session=session,
datatype='meg',
root=bids_root)
er_dir = er_dir_path.mkdir().directory
meas_date = (datetime.strptime(session,
'%Y%m%d').replace(tzinfo=timezone.utc))
raw = _read_raw_fif(raw_fname)
er_raw_fname = op.join(data_path, 'MEG', 'sample', 'ernoise_raw.fif')
raw.copy().crop(0, 10).save(er_raw_fname, overwrite=True)
er_raw = _read_raw_fif(er_raw_fname)
if check_version('mne', '0.20'):
raw.set_meas_date(meas_date)
er_raw.set_meas_date(meas_date)
else:
raw.info['meas_date'] = (meas_date.timestamp(), 0)
er_raw.info['meas_date'] = (meas_date.timestamp(), 0)
write_raw_bids(raw, bids_path, overwrite=True)
er_bids_path = BIDSPath(subject='emptyroom', session=session)
er_basename_1 = er_bids_path.basename
er_basename_2 = BIDSPath(subject='emptyroom',
session=session,
task='noise').basename
er_raw.save(op.join(er_dir, f'{er_basename_1}_meg.fif'))
er_raw.save(op.join(er_dir, f'{er_basename_2}_meg.fif'))
with pytest.warns(RuntimeWarning, match='Found more than one'):
bids_path.find_empty_room()
def test_plot_head_positions():
"""Test plotting of head positions."""
import matplotlib.pyplot as plt
info = read_info(evoked_fname)
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
destination = (0., 0., 0.04)
with warnings.catch_warnings(record=True): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos, mode='field', info=info,
destination=destination)
else:
assert_raises(RuntimeError, plot_head_positions, pos, mode='field',
info=info, destination=destination)
plot_head_positions([pos, pos]) # list support
assert_raises(ValueError, plot_head_positions, ['pos'])
assert_raises(ValueError, plot_head_positions, pos[:, :9])
assert_raises(ValueError, plot_head_positions, pos, 'foo')
plt.close('all')
def test_plot_head_positions():
"""Test plotting of head positions."""
info = read_info(evoked_fname)
pos = np.random.RandomState(0).randn(4, 10)
pos[:, 0] = np.arange(len(pos))
destination = (0., 0., 0.04)
with pytest.warns(None): # old MPL will cause a warning
plot_head_positions(pos)
if check_version('matplotlib', '1.4'):
plot_head_positions(pos, mode='field', info=info,
destination=destination)
else:
pytest.raises(RuntimeError, plot_head_positions, pos, mode='field',
info=info, destination=destination)
plot_head_positions([pos, pos]) # list support
pytest.raises(ValueError, plot_head_positions, ['pos'])
pytest.raises(ValueError, plot_head_positions, pos[:, :9])
pytest.raises(ValueError, plot_head_positions, pos, 'foo')
with pytest.raises(ValueError, match='shape'):
with pytest.warns(None): # old mpl no viridis warning
plot_head_positions(pos, axes=1.)
plt.close('all')
def test_generalization_across_time():
"""Test time generalization decoding
"""
from sklearn.svm import SVC
from sklearn.base import is_classifier
# KernelRidge is used for testing 1) regression analyses 2) n-dimensional
# predictions.
from sklearn.kernel_ridge import KernelRidge
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import roc_auc_score, mean_squared_error
epochs = make_epochs()
y_4classes = np.hstack((epochs.events[:7, 2], epochs.events[7:, 2] + 1))
if check_version('sklearn', '0.18'):
from sklearn.model_selection import (KFold, StratifiedKFold,
ShuffleSplit, LeaveOneLabelOut)
cv_shuffle = ShuffleSplit()
cv = LeaveOneLabelOut()
# XXX we cannot pass any other parameters than X and y to cv.split
# so we have to build it before hand
cv_lolo = [(train, test) for train, test in cv.split(
X=y_4classes, y=y_4classes, labels=y_4classes)]
# With sklearn >= 0.17, `clf` can be identified as a regressor, and
# the scoring metrics can therefore be automatically assigned.
scorer_regress = None
else:
from sklearn.cross_validation import (KFold, StratifiedKFold,
ShuffleSplit, LeaveOneLabelOut)
cv_shuffle = ShuffleSplit(len(epochs))
cv_lolo = LeaveOneLabelOut(y_4classes)
# With sklearn < 0.17, `clf` cannot be identified as a regressor, and
# therefore the scoring metrics cannot be automatically assigned.
scorer_regress = mean_squared_error
# Test default running
gat = GeneralizationAcrossTime(picks='foo')
assert_equal("<GAT | no fit, no prediction, no score>", "%s" % gat)
assert_raises(ValueError, gat.fit, epochs)
with warnings.catch_warnings(record=True):
# check classic fit + check manual picks
gat.picks = [0]
gat.fit(epochs)
# check optional y as array
gat.picks = None
gat.fit(epochs, y=epochs.events[:, 2])
# check optional y as list
gat.fit(epochs, y=epochs.events[:, 2].tolist())
assert_equal(len(gat.picks_), len(gat.ch_names), 1)
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), no "
"prediction, no score>", '%s' % gat)
assert_equal(gat.ch_names, epochs.ch_names)
# test different predict function:
gat = GeneralizationAcrossTime(predict_method='decision_function')
gat.fit(epochs)
# With classifier, the default cv is StratifiedKFold
assert_true(gat.cv_.__class__ == StratifiedKFold)
gat.predict(epochs)
assert_array_equal(np.shape(gat.y_pred_), (15, 15, 14, 1))
gat.predict_method = 'predict_proba'
gat.predict(epochs)
assert_array_equal(np.shape(gat.y_pred_), (15, 15, 14, 2))
gat.predict_method = 'foo'
assert_raises(NotImplementedError, gat.predict, epochs)
gat.predict_method = 'predict'
gat.predict(epochs)
assert_array_equal(np.shape(gat.y_pred_), (15, 15, 14, 1))
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), "
"predicted 14 epochs, no score>",
"%s" % gat)
gat.score(epochs)
assert_true(gat.scorer_.__name__ == 'accuracy_score')
# check clf / predict_method combinations for which the scoring metrics
# cannot be inferred.
gat.scorer = None
gat.predict_method = 'decision_function'
assert_raises(ValueError, gat.score, epochs)
# Check specifying y manually
gat.predict_method = 'predict'
gat.score(epochs, y=epochs.events[:, 2])
gat.score(epochs, y=epochs.events[:, 2].tolist())
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), "
"predicted 14 epochs,\n scored "
"(accuracy_score)>", "%s" % gat)
with warnings.catch_warnings(record=True):
gat.fit(epochs, y=epochs.events[:, 2])
old_mode = gat.predict_mode
gat.predict_mode = 'super-foo-mode'
assert_raises(ValueError, gat.predict, epochs)
gat.predict_mode = old_mode
gat.score(epochs, y=epochs.events[:, 2])
assert_true("accuracy_score" in '%s' % gat.scorer_)
epochs2 = epochs.copy()
# check _DecodingTime class
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.050 (s), length: 0.050 (s), n_time_windows: 15>",
"%s" % gat.train_times_)
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.050 (s), length: 0.050 (s), n_time_windows: 15 x 15>",
"%s" % gat.test_times_)
# the y-check
gat.predict_mode = 'mean-prediction'
epochs2.events[:, 2] += 10
gat_ = copy.deepcopy(gat)
with use_log_level('error'):
assert_raises(ValueError, gat_.score, epochs2)
gat.predict_mode = 'cross-validation'
# Test basics
# --- number of trials
assert_true(gat.y_train_.shape[0] ==
gat.y_true_.shape[0] ==
len(gat.y_pred_[0][0]) == 14)
# --- number of folds
assert_true(np.shape(gat.estimators_)[1] == gat.cv)
# --- length training size
assert_true(len(gat.train_times_['slices']) == 15 ==
np.shape(gat.estimators_)[0])
# --- length testing sizes
assert_true(len(gat.test_times_['slices']) == 15 ==
np.shape(gat.scores_)[0])
assert_true(len(gat.test_times_['slices'][0]) == 15 ==
np.shape(gat.scores_)[1])
# Test score_mode
gat.score_mode = 'foo'
assert_raises(ValueError, gat.score, epochs)
gat.score_mode = 'fold-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15, 5])
gat.score_mode = 'mean-sample-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15])
gat.score_mode = 'mean-fold-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15])
gat.predict_mode = 'mean-prediction'
with warnings.catch_warnings(record=True) as w:
gat.score(epochs)
assert_true(any("score_mode changed from " in str(ww.message)
for ww in w))
# Test longer time window
gat = GeneralizationAcrossTime(train_times={'length': .100})
with warnings.catch_warnings(record=True):
gat2 = gat.fit(epochs)
assert_true(gat is gat2) # return self
assert_true(hasattr(gat2, 'cv_'))
assert_true(gat2.cv_ != gat.cv)
with warnings.catch_warnings(record=True): # not vectorizing
scores = gat.score(epochs)
assert_true(isinstance(scores, np.ndarray)) # type check
assert_equal(len(scores[0]), len(scores)) # shape check
assert_equal(len(gat.test_times_['slices'][0][0]), 2)
# Decim training steps
gat = GeneralizationAcrossTime(train_times={'step': .100})
with warnings.catch_warnings(record=True):
gat.fit(epochs)
gat.score(epochs)
assert_true(len(gat.scores_) == len(gat.estimators_) == 8) # training time
assert_equal(len(gat.scores_[0]), 15) # testing time
# Test start stop training & test cv without n_fold params
y_4classes = np.hstack((epochs.events[:7, 2], epochs.events[7:, 2] + 1))
train_times = dict(start=0.090, stop=0.250)
gat = GeneralizationAcrossTime(cv=cv_lolo, train_times=train_times)
# predict without fit
assert_raises(RuntimeError, gat.predict, epochs)
with warnings.catch_warnings(record=True):
gat.fit(epochs, y=y_4classes)
gat.score(epochs)
assert_equal(len(gat.scores_), 4)
assert_equal(gat.train_times_['times'][0], epochs.times[6])
assert_equal(gat.train_times_['times'][-1], epochs.times[9])
# Test score without passing epochs & Test diagonal decoding
gat = GeneralizationAcrossTime(test_times='diagonal')
with warnings.catch_warnings(record=True): # not vectorizing
gat.fit(epochs)
assert_raises(RuntimeError, gat.score)
with warnings.catch_warnings(record=True): # not vectorizing
gat.predict(epochs)
scores = gat.score()
assert_true(scores is gat.scores_)
assert_equal(np.shape(gat.scores_), (15, 1))
assert_array_equal([tim for ttime in gat.test_times_['times']
for tim in ttime], gat.train_times_['times'])
# Test generalization across conditions
gat = GeneralizationAcrossTime(predict_mode='mean-prediction', cv=2)
with warnings.catch_warnings(record=True):
gat.fit(epochs[0:6])
with warnings.catch_warnings(record=True):
# There are some empty test folds because of n_trials
gat.predict(epochs[7:])
gat.score(epochs[7:])
# Test training time parameters
gat_ = copy.deepcopy(gat)
# --- start stop outside time range
gat_.train_times = dict(start=-999.)
with use_log_level('error'):
assert_raises(ValueError, gat_.fit, epochs)
gat_.train_times = dict(start=999.)
assert_raises(ValueError, gat_.fit, epochs)
# --- impossible slices
gat_.train_times = dict(step=.000001)
assert_raises(ValueError, gat_.fit, epochs)
gat_.train_times = dict(length=.000001)
assert_raises(ValueError, gat_.fit, epochs)
gat_.train_times = dict(length=999.)
assert_raises(ValueError, gat_.fit, epochs)
# Test testing time parameters
# --- outside time range
gat.test_times = dict(start=-999.)
with warnings.catch_warnings(record=True): # no epochs in fold
assert_raises(ValueError, gat.predict, epochs)
gat.test_times = dict(start=999.)
with warnings.catch_warnings(record=True): # no test epochs
assert_raises(ValueError, gat.predict, epochs)
# --- impossible slices
gat.test_times = dict(step=.000001)
with warnings.catch_warnings(record=True): # no test epochs
assert_raises(ValueError, gat.predict, epochs)
gat_ = copy.deepcopy(gat)
gat_.train_times_['length'] = .000001
gat_.test_times = dict(length=.000001)
with warnings.catch_warnings(record=True): # no test epochs
assert_raises(ValueError, gat_.predict, epochs)
# --- test time region of interest
gat.test_times = dict(step=.150)
with warnings.catch_warnings(record=True): # not vectorizing
gat.predict(epochs)
assert_array_equal(np.shape(gat.y_pred_), (15, 5, 14, 1))
# --- silly value
gat.test_times = 'foo'
with warnings.catch_warnings(record=True): # no test epochs
assert_raises(ValueError, gat.predict, epochs)
assert_raises(RuntimeError, gat.score)
# --- unmatched length between training and testing time
gat.test_times = dict(length=.150)
assert_raises(ValueError, gat.predict, epochs)
# --- irregular length training and testing times
# 2 estimators, the first one is trained on two successive time samples
# whereas the second one is trained on a single time sample.
train_times = dict(slices=[[0, 1], [1]])
# The first estimator is tested once, the second estimator is tested on
# two successive time samples.
test_times = dict(slices=[[[0, 1]], [[0], [1]]])
gat = GeneralizationAcrossTime(train_times=train_times,
test_times=test_times)
gat.fit(epochs)
with warnings.catch_warnings(record=True): # not vectorizing
gat.score(epochs)
assert_array_equal(np.shape(gat.y_pred_[0]), [1, len(epochs), 1])
assert_array_equal(np.shape(gat.y_pred_[1]), [2, len(epochs), 1])
# check cannot Automatically infer testing times for adhoc training times
gat.test_times = None
assert_raises(ValueError, gat.predict, epochs)
svc = SVC(C=1, kernel='linear', probability=True)
gat = GeneralizationAcrossTime(clf=svc, predict_mode='mean-prediction')
with warnings.catch_warnings(record=True):
gat.fit(epochs)
# sklearn needs it: c.f.
# https://github.com/scikit-learn/scikit-learn/issues/2723
# and http://bit.ly/1u7t8UT
with use_log_level('error'):
assert_raises(ValueError, gat.score, epochs2)
gat.score(epochs)
assert_true(0.0 <= np.min(scores) <= 1.0)
assert_true(0.0 <= np.max(scores) <= 1.0)
# Test that gets error if train on one dataset, test on another, and don't
# specify appropriate cv:
gat = GeneralizationAcrossTime(cv=cv_shuffle)
gat.fit(epochs)
with warnings.catch_warnings(record=True):
gat.fit(epochs)
gat.predict(epochs)
assert_raises(ValueError, gat.predict, epochs[:10])
# Make CV with some empty train and test folds:
# --- empty test fold(s) should warn when gat.predict()
gat._cv_splits[0] = [gat._cv_splits[0][0], np.empty(0)]
with warnings.catch_warnings(record=True) as w:
gat.predict(epochs)
assert_true(len(w) > 0)
assert_true(any('do not have any test epochs' in str(ww.message)
for ww in w))
# --- empty train fold(s) should raise when gat.fit()
gat = GeneralizationAcrossTime(cv=[([0], [1]), ([], [0])])
assert_raises(ValueError, gat.fit, epochs[:2])
# Check that still works with classifier that output y_pred with
# shape = (n_trials, 1) instead of (n_trials,)
if check_version('sklearn', '0.17'): # no is_regressor before v0.17
gat = GeneralizationAcrossTime(clf=KernelRidge(), cv=2)
epochs.crop(None, epochs.times[2])
gat.fit(epochs)
# With regression the default cv is KFold and not StratifiedKFold
assert_true(gat.cv_.__class__ == KFold)
gat.score(epochs)
# with regression the default scoring metrics is mean squared error
assert_true(gat.scorer_.__name__ == 'mean_squared_error')
# Test combinations of complex scenarios
# 2 or more distinct classes
n_classes = [2, 4] # 4 tested
# nicely ordered labels or not
le = LabelEncoder()
y = le.fit_transform(epochs.events[:, 2])
y[len(y) // 2:] += 2
ys = (y, y + 1000)
# Univariate and multivariate prediction
svc = SVC(C=1, kernel='linear', probability=True)
reg = KernelRidge()
def scorer_proba(y_true, y_pred):
return roc_auc_score(y_true, y_pred[:, 0])
# We re testing 3 scenario: default, classifier + predict_proba, regressor
scorers = [None, scorer_proba, scorer_regress]
predict_methods = [None, 'predict_proba', None]
clfs = [svc, svc, reg]
# Test all combinations
for clf, predict_method, scorer in zip(clfs, predict_methods, scorers):
for y in ys:
for n_class in n_classes:
for predict_mode in ['cross-validation', 'mean-prediction']:
# Cannot use AUC for n_class > 2
if (predict_method == 'predict_proba' and n_class != 2):
continue
y_ = y % n_class
with warnings.catch_warnings(record=True):
gat = GeneralizationAcrossTime(
cv=2, clf=clf, scorer=scorer,
predict_mode=predict_mode)
gat.fit(epochs, y=y_)
gat.score(epochs, y=y_)
# Check that scorer is correctly defined manually and
# automatically.
scorer_name = gat.scorer_.__name__
if scorer is None:
if is_classifier(clf):
assert_equal(scorer_name, 'accuracy_score')
else:
assert_equal(scorer_name, 'mean_squared_error')
else:
assert_equal(scorer_name, scorer.__name__)
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))
