def test_evoked_proj():
"""Test SSP proj operations
"""
for proj in [True, False]:
ave = read_evoked(fname, setno=0, proj=proj)
assert_true(all(p['active'] == proj for p in ave.info['projs']))
# test adding / deleting proj
if proj:
assert_raises(ValueError, ave.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, ave.del_proj, 0)
else:
projs = deepcopy(ave.info['projs'])
n_proj = len(ave.info['projs'])
ave.del_proj(0)
assert_true(len(ave.info['projs']) == n_proj - 1)
ave.add_proj(projs, remove_existing=False)
assert_true(len(ave.info['projs']) == 2 * n_proj - 1)
ave.add_proj(projs, remove_existing=True)
assert_true(len(ave.info['projs']) == n_proj)
ave = read_evoked(fname, setno=0, proj=False)
data = ave.data.copy()
ave.apply_proj()
assert_allclose(np.dot(ave._projector, data), ave.data)
def test_evoked_io_from_epochs():
"""Test IO of evoked data made from epochs
"""
# offset our tmin so we don't get exactly a zero value when decimating
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax, picks=picks, baseline=(None, 0), decim=5)
assert_true(len(w) == 1)
evoked = epochs.average()
evoked.save(op.join(tempdir, "evoked.fif"))
evoked2 = read_evoked(op.join(tempdir, "evoked.fif"))
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1 / evoked.info["sfreq"])
# now let's do one with negative time
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, 0.1, tmax, picks=picks, baseline=(0.1, 0.2), decim=5)
evoked = epochs.average()
evoked.save(op.join(tempdir, "evoked.fif"))
evoked2 = read_evoked(op.join(tempdir, "evoked.fif"))
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)
# should be equivalent to a cropped original
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, -0.2, tmax, picks=picks, baseline=(0.1, 0.2), decim=5)
evoked = epochs.average()
evoked.crop(0.099, None)
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)
def test_evoked_standard_error():
"""Test calculation and read/write of standard error
"""
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0))
evoked = [epochs.average(), epochs.standard_error()]
fiff.write_evoked(op.join(tempdir, 'evoked.fif'), evoked)
evoked2 = read_evoked(op.join(tempdir, 'evoked.fif'), [0, 1])
evoked3 = [
read_evoked(op.join(tempdir, 'evoked.fif'), 'Unknown'),
read_evoked(op.join(tempdir, 'evoked.fif'),
'Unknown',
kind='standard_error')
]
for evoked_new in [evoked2, evoked3]:
assert_true(
evoked_new[0]._aspect_kind == fiff.FIFF.FIFFV_ASPECT_AVERAGE)
assert_true(evoked_new[0].kind == 'average')
assert_true(
evoked_new[1]._aspect_kind == fiff.FIFF.FIFFV_ASPECT_STD_ERR)
assert_true(evoked_new[1].kind == 'standard_error')
for ave, ave2 in zip(evoked, evoked_new):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_evoked_proj():
"""Test SSP proj operations
"""
for proj in [True, False]:
ave = read_evoked(fname, setno=0, proj=proj)
assert_true(all(p['active'] == proj for p in ave.info['projs']))
# test adding / deleting proj
if proj:
assert_raises(ValueError, ave.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, ave.del_proj, 0)
else:
projs = deepcopy(ave.info['projs'])
n_proj = len(ave.info['projs'])
ave.del_proj(0)
assert_true(len(ave.info['projs']) == n_proj - 1)
ave.add_proj(projs, remove_existing=False)
assert_true(len(ave.info['projs']) == 2 * n_proj - 1)
ave.add_proj(projs, remove_existing=True)
assert_true(len(ave.info['projs']) == n_proj)
ave = read_evoked(fname, setno=0, proj=False)
data = ave.data.copy()
ave.apply_proj()
assert_allclose(np.dot(ave._projector, data), ave.data)
def test_evoked_resample():
"""Test for resampling of evoked data
"""
# upsample, write it out, read it in
ave = read_evoked(fname, 0)
sfreq_normal = ave.info['sfreq']
ave.resample(2 * sfreq_normal)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave_up = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
# compare it to the original
ave_normal = read_evoked(fname, 0)
# and compare the original to the downsampled upsampled version
ave_new = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_new.resample(sfreq_normal)
assert_array_almost_equal(ave_normal.data, ave_new.data, 2)
assert_array_almost_equal(ave_normal.times, ave_new.times)
assert_equal(ave_normal.nave, ave_new.nave)
assert_equal(ave_normal._aspect_kind, ave_new._aspect_kind)
assert_equal(ave_normal.kind, ave_new.kind)
assert_equal(ave_normal.last, ave_new.last)
assert_equal(ave_normal.first, ave_new.first)
# for the above to work, the upsampling just about had to, but
# we'll add a couple extra checks anyway
assert_true(len(ave_up.times) == 2 * len(ave_normal.times))
assert_true(ave_up.data.shape[1] == 2 * ave_normal.data.shape[1])
def test_io_evoked():
"""Test IO for evoked data (fif + gz) with integer and str args
"""
ave = read_evoked(fname, 0)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave2 = read_evoked(op.join(tempdir, 'evoked.fif'))
# This not being assert_array_equal due to windows rounding
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
# test compressed i/o
ave2 = read_evoked(fname_gz, 0)
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-8))
# test str access
setno = 'Left Auditory'
assert_raises(ValueError, read_evoked, fname, setno, kind='stderr')
assert_raises(ValueError, read_evoked, fname, setno, kind='standard_error')
ave3 = read_evoked(fname, setno)
assert_array_almost_equal(ave.data, ave3.data, 19)
def test_io_evoked():
"""Test IO for evoked data (fif + gz) with integer and str args
"""
ave = read_evoked(fname, 0)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave2 = read_evoked(op.join(tempdir, 'evoked.fif'))
# This not being assert_array_equal due to windows rounding
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
# test compressed i/o
ave2 = read_evoked(fname_gz, 0)
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-8))
# test str access
setno = 'Left Auditory'
assert_raises(ValueError, read_evoked, fname, setno, kind='stderr')
assert_raises(ValueError, read_evoked, fname, setno, kind='standard_error')
ave3 = read_evoked(fname, setno)
assert_array_almost_equal(ave.data, ave3.data, 19)
def test_evoked_standard_error():
"""Test calculation and read/write of standard error
"""
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
evoked = [epochs.average(), epochs.standard_error()]
fiff.write_evoked(op.join(tempdir, 'evoked.fif'), evoked)
evoked2 = read_evoked(op.join(tempdir, 'evoked.fif'), [0, 1])
evoked3 = [read_evoked(op.join(tempdir, 'evoked.fif'), 'Unknown'),
read_evoked(op.join(tempdir, 'evoked.fif'), 'Unknown',
kind='standard_error')]
for evoked_new in [evoked2, evoked3]:
assert_true(evoked_new[0]._aspect_kind ==
fiff.FIFF.FIFFV_ASPECT_AVERAGE)
assert_true(evoked_new[0].kind == 'average')
assert_true(evoked_new[1]._aspect_kind ==
fiff.FIFF.FIFFV_ASPECT_STD_ERR)
assert_true(evoked_new[1].kind == 'standard_error')
for ave, ave2 in zip(evoked, evoked_new):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_evoked_io_from_epochs():
"""Test IO of evoked data made from epochs
"""
# offset our tmin so we don't get exactly a zero value when decimating
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, tmin + 0.011, tmax,
picks=picks, baseline=(None, 0), decim=5)
assert_true(len(w) == 1)
evoked = epochs.average()
evoked.save(op.join(tempdir, 'evoked.fif'))
evoked2 = read_evoked(op.join(tempdir, 'evoked.fif'))
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4,
atol=1 / evoked.info['sfreq'])
# now let's do one with negative time
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, 0.1, tmax,
picks=picks, baseline=(0.1, 0.2), decim=5)
evoked = epochs.average()
evoked.save(op.join(tempdir, 'evoked.fif'))
evoked2 = read_evoked(op.join(tempdir, 'evoked.fif'))
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)
# should be equivalent to a cropped original
with warnings.catch_warnings(True) as w:
epochs = Epochs(raw, events[:4], event_id, -0.2, tmax,
picks=picks, baseline=(0.1, 0.2), decim=5)
evoked = epochs.average()
evoked.crop(0.099, None)
assert_allclose(evoked.data, evoked2.data, rtol=1e-4, atol=1e-20)
assert_allclose(evoked.times, evoked2.times, rtol=1e-4, atol=1e-20)
def test_evoked_resample():
"""Test for resampling of evoked data
"""
# upsample, write it out, read it in
ave = read_evoked(fname, 0)
sfreq_normal = ave.info['sfreq']
ave.resample(2 * sfreq_normal)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave_up = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
# compare it to the original
ave_normal = read_evoked(fname, 0)
# and compare the original to the downsampled upsampled version
ave_new = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_new.resample(sfreq_normal)
assert_array_almost_equal(ave_normal.data, ave_new.data, 2)
assert_array_almost_equal(ave_normal.times, ave_new.times)
assert_equal(ave_normal.nave, ave_new.nave)
assert_equal(ave_normal._aspect_kind, ave_new._aspect_kind)
assert_equal(ave_normal.kind, ave_new.kind)
assert_equal(ave_normal.last, ave_new.last)
assert_equal(ave_normal.first, ave_new.first)
# for the above to work, the upsampling just about had to, but
# we'll add a couple extra checks anyway
assert_true(len(ave_up.times) == 2 * len(ave_normal.times))
assert_true(ave_up.data.shape[1] == 2 * ave_normal.data.shape[1])
def test_evoked_to_nitime():
""" Test to_nitime """
aves = read_evoked(fname, [0, 1, 2, 3])
evoked_ts = aves[0].to_nitime()
assert_equal(evoked_ts.data, aves[0].data)
picks2 = [1, 2]
aves = read_evoked(fname, [0, 1, 2, 3])
evoked_ts = aves[0].to_nitime(picks=picks2)
assert_equal(evoked_ts.data, aves[0].data[picks2])
def test_evoked_to_nitime():
""" Test to_nitime """
aves = read_evoked(fname, [0, 1, 2, 3])
evoked_ts = aves[0].to_nitime()
assert_equal(evoked_ts.data, aves[0].data)
picks2 = [1, 2]
aves = read_evoked(fname, [0, 1, 2, 3])
evoked_ts = aves[0].to_nitime(picks=picks2)
assert_equal(evoked_ts.data, aves[0].data[picks2])
def test_evoked_detrend():
"""Test for detrending evoked data
"""
ave = read_evoked(fname, 0)
ave_normal = read_evoked(fname, 0)
ave.detrend(0)
ave_normal.data -= np.mean(ave_normal.data, axis=1)[:, np.newaxis]
picks = pick_types(ave.info, meg=True, eeg=True, exclude='bads')
assert_true(np.allclose(ave.data[picks], ave_normal.data[picks],
rtol=1e-8, atol=1e-16))
def test_evoked_detrend():
"""Test for detrending evoked data
"""
ave = read_evoked(fname, 0)
ave_normal = read_evoked(fname, 0)
ave.detrend(0)
ave_normal.data -= np.mean(ave_normal.data, axis=1)[:, np.newaxis]
picks = pick_types(ave.info, meg=True, eeg=True, exclude='bads')
assert_true(np.allclose(ave.data[picks], ave_normal.data[picks],
rtol=1e-8, atol=1e-16))
def test_io_multi_evoked():
"""Test IO for multiple evoked datasets
"""
aves = read_evoked(fname, [0, 1, 2, 3])
write_evoked('evoked.fif', aves)
aves2 = read_evoked('evoked.fif', [0, 1, 2, 3])
for [ave, ave2] in zip(aves, aves2):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave.aspect_kind, ave2.aspect_kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_io_evoked():
"""Test IO for evoked data
"""
ave = read_evoked(fname)
ave.crop(tmin=0)
write_evoked('evoked.fif', ave)
ave2 = read_evoked('evoked.fif')
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave.aspect_kind, ave2.aspect_kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_meg_field_interpolation_helmet():
"""Test interpolation of MEG field onto helmet
"""
evoked = read_evoked(evoked_fname, setno='Left Auditory')
info = evoked.info
surf = get_meg_helmet_surf(info)
# let's reduce the number of channels by a bunch to speed it up
info['bads'] = info['ch_names'][:200]
# bad ch_type
assert_raises(ValueError, make_surface_mapping, info, surf, 'foo')
# bad mode
assert_raises(ValueError, make_surface_mapping, info, surf, 'meg',
mode='foo')
# no picks
evoked_eeg = pick_types_evoked(evoked, meg=False, eeg=True)
assert_raises(RuntimeError, make_surface_mapping, evoked_eeg.info,
surf, 'meg')
# bad surface def
nn = surf['nn']
del surf['nn']
assert_raises(KeyError, make_surface_mapping, info, surf, 'meg')
surf['nn'] = nn
cf = surf['coord_frame']
del surf['coord_frame']
assert_raises(KeyError, make_surface_mapping, info, surf, 'meg')
surf['coord_frame'] = cf
# now do it
data = make_surface_mapping(info, surf, 'meg', mode='fast')
assert_array_equal(data.shape, (304, 106)) # data onto surf
def test_io_evoked():
"""Test IO for evoked data (fif + gz) with integer and str args
"""
ave = read_evokeds(fname, 0)
write_evokeds(op.join(tempdir, 'evoked.fif'), ave)
ave2 = read_evokeds(op.join(tempdir, 'evoked.fif'))[0]
# This not being assert_array_equal due to windows rounding
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
# test compressed i/o
ave2 = read_evokeds(fname_gz, 0)
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-8))
# test str access
condition = 'Left Auditory'
assert_raises(ValueError, read_evokeds, fname, condition, kind='stderr')
assert_raises(ValueError,
read_evokeds,
fname,
condition,
kind='standard_error')
ave3 = read_evokeds(fname, condition)
assert_array_almost_equal(ave.data, ave3.data, 19)
# test deprecation warning for read_evoked and write_evoked
# XXX should be deleted for 0.9 release
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
ave = read_evoked(fname, setno=0)
assert_true(w[0].category == DeprecationWarning)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
assert_true(w[0].category == DeprecationWarning)
# test read_evokeds and write_evokeds
types = ['Left Auditory', 'Right Auditory', 'Left visual', 'Right visual']
aves1 = read_evokeds(fname)
aves2 = read_evokeds(fname, [0, 1, 2, 3])
aves3 = read_evokeds(fname, types)
write_evokeds(op.join(tempdir, 'evoked.fif'), aves1)
aves4 = read_evokeds(op.join(tempdir, 'evoked.fif'))
for aves in [aves2, aves3, aves4]:
for [av1, av2] in zip(aves1, aves):
assert_array_almost_equal(av1.data, av2.data)
assert_array_almost_equal(av1.times, av2.times)
assert_equal(av1.nave, av2.nave)
assert_equal(av1.kind, av2.kind)
assert_equal(av1._aspect_kind, av2._aspect_kind)
assert_equal(av1.last, av2.last)
assert_equal(av1.first, av2.first)
assert_equal(av1.comment, av2.comment)
def test_plot_topomap():
"""Testing topomap plotting
"""
# evoked
evoked = fiff.read_evoked(evoked_fname, 'Left Auditory',
baseline=(None, 0))
evoked.plot_topomap(0.1, 'mag', layout=layout)
plot_evoked_topomap(evoked, None, ch_type='mag')
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type='eeg')
plot_evoked_topomap(evoked, times, ch_type='grad')
plot_evoked_topomap(evoked, times, ch_type='planar1')
plot_evoked_topomap(evoked, times, ch_type='planar2')
with warnings.catch_warnings(True): # delaunay triangulation warning
plot_evoked_topomap(evoked, times, ch_type='mag', layout='auto')
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
evoked.proj = False # let's fake it like they haven't been applied
plot_evoked_topomap(evoked, 0.1, 'mag', proj='interactive')
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
# projs
projs = read_proj(ecg_fname)[:7]
plot_projs_topomap(projs)
plt.close('all')
def test_plot_topomap():
"""Testing topomap plotting
"""
# evoked
evoked = fiff.read_evoked(evoked_fname,
'Left Auditory',
baseline=(None, 0))
evoked.plot_topomap(0.1, 'mag', layout=layout)
plot_evoked_topomap(evoked, None, ch_type='mag')
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type='eeg')
plot_evoked_topomap(evoked, times, ch_type='grad')
plot_evoked_topomap(evoked, times, ch_type='planar1')
plot_evoked_topomap(evoked, times, ch_type='planar2')
with warnings.catch_warnings(True): # delaunay triangulation warning
plot_evoked_topomap(evoked, times, ch_type='mag', layout='auto')
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
evoked.proj = False # let's fake it like they haven't been applied
plot_evoked_topomap(evoked, 0.1, 'mag', proj='interactive')
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
# projs
projs = read_proj(ecg_fname)[:7]
plot_projs_topomap(projs)
plt.close('all')
def test_plot_topomap():
"""Test topomap plotting
"""
# evoked
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
evoked = fiff.read_evoked(evoked_fname, 'Left Auditory',
baseline=(None, 0))
evoked.plot_topomap(0.1, 'mag', layout=layout)
plot_evoked_topomap(evoked, None, ch_type='mag')
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type='eeg')
plot_evoked_topomap(evoked, times, ch_type='grad')
plot_evoked_topomap(evoked, times, ch_type='planar1')
plot_evoked_topomap(evoked, times, ch_type='planar2')
plot_evoked_topomap(evoked, times, ch_type='grad', show_names=True)
p = plot_evoked_topomap(evoked, times, ch_type='grad',
show_names=lambda x: x.replace('MEG', ''))
subplot = [x for x in p.get_children() if
isinstance(x, matplotlib.axes.Subplot)][0]
assert_true(all('MEG' not in x.get_text()
for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [x.get_text() for x in p.get_children() if
isinstance(x, matplotlib.text.Text)]
p = plot_evoked_topomap(evoked, times, ch_type='eeg')
assert_equal(len(get_texts(p)), 0)
p = plot_evoked_topomap(evoked, times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
# delaunay triangulation warning
with warnings.catch_warnings(record=True):
plot_evoked_topomap(evoked, times, ch_type='mag', layout='auto')
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
evoked.proj = False # let's fake it like they haven't been applied
plot_evoked_topomap(evoked, 0.1, 'mag', proj='interactive')
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
projs = read_proj(ecg_fname)
projs = [p for p in projs if p['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs)
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == fiff.FIFF.FIFFV_COIL_EEG:
if ch['eeg_loc'] is not None:
ch['eeg_loc'].fill(0)
ch['loc'].fill(0)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
def compensate_mne(fname, comp):
tmp_fname = '%s-%d.fif' % (fname[:-4], comp)
cmd = [
'mne_compensate_data', '--in', fname, '--out', tmp_fname, '--grad',
str(comp)
]
run_subprocess(cmd)
return read_evoked(tmp_fname)
def test_drop_channels_mixin():
"""Test channels-dropping functionality
"""
evoked = read_evoked(fname, setno=0, proj=True)
drop_ch = evoked.ch_names[:3]
ch_names = evoked.ch_names[3:]
evoked.drop_channels(drop_ch)
assert_equal(ch_names, evoked.ch_names)
assert_equal(len(ch_names), len(evoked.data))
def test_drop_channels_mixin():
"""Test channels-dropping functionality
"""
evoked = read_evoked(fname, setno=0, proj=True)
drop_ch = evoked.ch_names[:3]
ch_names = evoked.ch_names[3:]
evoked.drop_channels(drop_ch)
assert_equal(ch_names, evoked.ch_names)
assert_equal(len(ch_names), len(evoked.data))
def test_io_evoked():
"""Test IO for evoked data (fif + gz) with integer and str args
"""
ave = read_evokeds(fname, 0)
write_evokeds(op.join(tempdir, 'evoked.fif'), ave)
ave2 = read_evokeds(op.join(tempdir, 'evoked.fif'))[0]
# This not being assert_array_equal due to windows rounding
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave._aspect_kind, ave2._aspect_kind)
assert_equal(ave.kind, ave2.kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
# test compressed i/o
ave2 = read_evokeds(fname_gz, 0)
assert_true(np.allclose(ave.data, ave2.data, atol=1e-16, rtol=1e-8))
# test str access
condition = 'Left Auditory'
assert_raises(ValueError, read_evokeds, fname, condition, kind='stderr')
assert_raises(ValueError, read_evokeds, fname, condition,
kind='standard_error')
ave3 = read_evokeds(fname, condition)
assert_array_almost_equal(ave.data, ave3.data, 19)
# test deprecation warning for read_evoked and write_evoked
# XXX should be deleted for 0.9 release
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
ave = read_evoked(fname, setno=0)
assert_true(w[0].category == DeprecationWarning)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
assert_true(w[0].category == DeprecationWarning)
# test read_evokeds and write_evokeds
types = ['Left Auditory', 'Right Auditory', 'Left visual', 'Right visual']
aves1 = read_evokeds(fname)
aves2 = read_evokeds(fname, [0, 1, 2, 3])
aves3 = read_evokeds(fname, types)
write_evokeds(op.join(tempdir, 'evoked.fif'), aves1)
aves4 = read_evokeds(op.join(tempdir, 'evoked.fif'))
for aves in [aves2, aves3, aves4]:
for [av1, av2] in zip(aves1, aves):
assert_array_almost_equal(av1.data, av2.data)
assert_array_almost_equal(av1.times, av2.times)
assert_equal(av1.nave, av2.nave)
assert_equal(av1.kind, av2.kind)
assert_equal(av1._aspect_kind, av2._aspect_kind)
assert_equal(av1.last, av2.last)
assert_equal(av1.first, av2.first)
assert_equal(av1.comment, av2.comment)
def test_as_data_frame():
"""Test evoked Pandas exporter"""
ave = read_evoked(fname, [0])[0]
assert_raises(ValueError, ave.as_data_frame, picks=np.arange(400))
df = ave.as_data_frame()
assert_true((df.columns == ave.ch_names).all())
df = ave.as_data_frame(use_time_index=False)
assert_true('time' in df.columns)
assert_array_equal(df.values[:, 1], ave.data[0] * 1e13)
assert_array_equal(df.values[:, 3], ave.data[2] * 1e15)
def test_io_multi_evoked():
"""Test IO for multiple evoked datasets
"""
aves = read_evoked(fname, [0, 1, 2, 3])
write_evoked(op.join(tempdir, 'evoked.fif'), aves)
aves2 = read_evoked(op.join(tempdir, 'evoked.fif'), [0, 1, 2, 3])
types = ['Left Auditory', 'Right Auditory', 'Left visual', 'Right visual']
aves3 = read_evoked(op.join(tempdir, 'evoked.fif'), types)
for aves_new in [aves2, aves3]:
for [ave, ave_new] in zip(aves, aves_new):
assert_array_almost_equal(ave.data, ave_new.data)
assert_array_almost_equal(ave.times, ave_new.times)
assert_equal(ave.nave, ave_new.nave)
assert_equal(ave.kind, ave_new.kind)
assert_equal(ave._aspect_kind, ave_new._aspect_kind)
assert_equal(ave.last, ave_new.last)
assert_equal(ave.first, ave_new.first)
# this should throw an error since there are mulitple datasets
assert_raises(ValueError, read_evoked, fname)
def test_as_data_frame():
"""Test Pandas exporter"""
ave = read_evoked(fname, [0])[0]
assert_raises(ValueError, ave.as_data_frame, picks=np.arange(400))
df = ave.as_data_frame()
assert_true((df.columns == ave.ch_names).all())
df = ave.as_data_frame(use_time_index=False)
assert_true('time' in df.columns)
assert_array_equal(df.values[:, 1], ave.data[0] * 1e13)
assert_array_equal(df.values[:, 3], ave.data[2] * 1e15)
def test_io_multi_evoked():
"""Test IO for multiple evoked datasets
"""
aves = read_evoked(fname, [0, 1, 2, 3])
write_evoked(op.join(tempdir, 'evoked.fif'), aves)
aves2 = read_evoked(op.join(tempdir, 'evoked.fif'), [0, 1, 2, 3])
types = ['Left Auditory', 'Right Auditory', 'Left visual', 'Right visual']
aves3 = read_evoked(op.join(tempdir, 'evoked.fif'), types)
for aves_new in [aves2, aves3]:
for [ave, ave_new] in zip(aves, aves_new):
assert_array_almost_equal(ave.data, ave_new.data)
assert_array_almost_equal(ave.times, ave_new.times)
assert_equal(ave.nave, ave_new.nave)
assert_equal(ave.kind, ave_new.kind)
assert_equal(ave._aspect_kind, ave_new._aspect_kind)
assert_equal(ave.last, ave_new.last)
assert_equal(ave.first, ave_new.first)
# this should throw an error since there are mulitple datasets
assert_raises(ValueError, read_evoked, fname)
def test_equalize_channels():
"""Test equalization of channels
"""
evoked1 = read_evoked(fname, setno=0, proj=True)
evoked2 = evoked1.copy()
ch_names = evoked1.ch_names[2:]
evoked1.drop_channels(evoked1.ch_names[:1])
evoked2.drop_channels(evoked2.ch_names[1:2])
my_comparison = [evoked1, evoked2]
equalize_channels(my_comparison)
for e in my_comparison:
assert_equal(ch_names, e.ch_names)
def test_equalize_channels():
"""Test equalization of channels
"""
evoked1 = read_evoked(fname, setno=0, proj=True)
evoked2 = evoked1.copy()
ch_names = evoked1.ch_names[2:]
evoked1.drop_channels(evoked1.ch_names[:1])
evoked2.drop_channels(evoked2.ch_names[1:2])
my_comparison = [evoked1, evoked2]
equalize_channels(my_comparison)
for e in my_comparison:
assert_equal(ch_names, e.ch_names)
def extract_data(s, setno, ch_names): print "hello" data = fiff.read_evoked(s,setno=setno,baseline=(None, 0)) sel = [data['info']['ch_names'].index(c) for c in ch_names] print "hello3" times = data['evoked']['times'] mask = times > 0 print "-------------- " + str(data['info']['bads']) epochs = data['evoked']['epochs'][sel][:, mask] bads = [k for k, _ in enumerate(sel) if ch_names[k] in data['info']['bads']] bads_name = [ch_names[k] for k, _ in enumerate(sel) if ch_names[k] in data['info']['bads']] return epochs, times[mask], bads, bads_name
def test_io_evoked():
"""Test IO for evoked data (fif + gz)
"""
ave = read_evoked(fname)
ave.crop(tmin=0)
write_evoked('evoked.fif', ave)
ave2 = read_evoked('evoked.fif')
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave.aspect_kind, ave2.aspect_kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
# test compressed i/o
ave2 = read_evoked(fname_gz)
ave2.crop(tmin=0)
assert_array_equal(ave.data, ave2.data)
def test_plot_evoked_field():
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
setno = 'Left Auditory'
evoked = fiff.read_evoked(evoked_fname, setno=setno,
baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
maps = make_field_map(evoked, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir,
n_jobs=1, ch_type=t)
evoked.plot_field(maps, time=0.1)
def test_plot_evoked_field():
trans_fname = op.join(data_dir, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
setno = 'Left Auditory'
evoked = fiff.read_evoked(evoked_fname, setno=setno, baseline=(-0.2, 0.0))
evoked = pick_channels_evoked(evoked, evoked.ch_names[::10]) # speed
for t in ['meg', None]:
maps = make_field_map(evoked,
trans_fname=trans_fname,
subject='sample',
subjects_dir=subjects_dir,
n_jobs=1,
ch_type=t)
evoked.plot_field(maps, time=0.1)
def extract_data(s, setno, ch_names):
print "hello"
data = fiff.read_evoked(s, setno=setno, baseline=(None, 0))
sel = [data['info']['ch_names'].index(c) for c in ch_names]
print "hello3"
times = data['evoked']['times']
mask = times > 0
print "-------------- " + str(data['info']['bads'])
epochs = data['evoked']['epochs'][sel][:, mask]
bads = [
k for k, _ in enumerate(sel) if ch_names[k] in data['info']['bads']
]
bads_name = [
ch_names[k] for k, _ in enumerate(sel)
if ch_names[k] in data['info']['bads']
]
return epochs, times[mask], bads, bads_name
def test_evoked_standard_error():
"""Test calculation and read/write of standard error
"""
epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
baseline=(None, 0))
evoked = [epochs.average(), epochs.standard_error()]
fiff.write_evoked('evoked.fif', evoked)
evoked2 = fiff.read_evoked('evoked.fif', [0, 1])
assert_true(evoked2[0].aspect_kind == fiff.FIFF.FIFFV_ASPECT_AVERAGE)
assert_true(evoked2[1].aspect_kind == fiff.FIFF.FIFFV_ASPECT_STD_ERR)
for ave, ave2 in zip(evoked, evoked2):
assert_array_almost_equal(ave.data, ave2.data)
assert_array_almost_equal(ave.times, ave2.times)
assert_equal(ave.nave, ave2.nave)
assert_equal(ave.aspect_kind, ave2.aspect_kind)
assert_equal(ave.last, ave2.last)
assert_equal(ave.first, ave2.first)
def test_plot_topomap():
"""Testing topomap plotting
"""
# evoked
evoked = fiff.read_evoked(evoked_fname, "Left Auditory", baseline=(None, 0))
evoked.plot_topomap(0.1, "mag", layout=layout)
plot_evoked_topomap(evoked, None, ch_type="mag")
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type="grad")
plot_evoked_topomap(evoked, times, ch_type="planar1")
plot_evoked_topomap(evoked, times, ch_type="mag", layout="auto")
plot_evoked_topomap(evoked, 0.1, "mag", proj="interactive")
assert_raises(RuntimeError, plot_evoked_topomap, evoked, np.repeat(0.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
# projs
projs = read_proj(ecg_fname)[:7]
plot_projs_topomap(projs)
def test_get_peak():
"""Test peak getter
"""
evoked = read_evoked(fname, setno=0, proj=True)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', tmin=1)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', tmax=0.9)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', tmin=0.02,
tmax=0.01)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', mode='foo')
assert_raises(RuntimeError, evoked.get_peak, ch_type=None, mode='foo')
assert_raises(ValueError, evoked.get_peak, ch_type='misc', mode='foo')
ch_idx, time_idx = evoked.get_peak(ch_type='mag')
assert_true(ch_idx in evoked.ch_names)
assert_true(time_idx in evoked.times)
ch_idx, time_idx = evoked.get_peak(ch_type='mag',
time_as_index=True)
assert_true(time_idx < len(evoked.times))
data = np.array([[0., 1., 2.],
[0., -3., 0]])
times = np.array([.1, .2, .3])
ch_idx, time_idx = _get_peak(data, times, mode='abs')
assert_equal(ch_idx, 1)
assert_equal(time_idx, 1)
ch_idx, time_idx = _get_peak(data * -1, times, mode='neg')
assert_equal(ch_idx, 0)
assert_equal(time_idx, 2)
ch_idx, time_idx = _get_peak(data, times, mode='pos')
assert_equal(ch_idx, 0)
assert_equal(time_idx, 2)
assert_raises(ValueError, _get_peak, data + 1e3, times, mode='neg')
assert_raises(ValueError, _get_peak, data - 1e3, times, mode='pos')
def test_get_peak():
"""Test peak getter
"""
evoked = read_evoked(fname, setno=0, proj=True)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', tmin=1)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', tmax=0.9)
assert_raises(ValueError,
evoked.get_peak,
ch_type='mag',
tmin=0.02,
tmax=0.01)
assert_raises(ValueError, evoked.get_peak, ch_type='mag', mode='foo')
assert_raises(RuntimeError, evoked.get_peak, ch_type=None, mode='foo')
assert_raises(ValueError, evoked.get_peak, ch_type='misc', mode='foo')
ch_idx, time_idx = evoked.get_peak(ch_type='mag')
assert_true(ch_idx in evoked.ch_names)
assert_true(time_idx in evoked.times)
ch_idx, time_idx = evoked.get_peak(ch_type='mag', time_as_index=True)
assert_true(time_idx < len(evoked.times))
data = np.array([[0., 1., 2.], [0., -3., 0]])
times = np.array([.1, .2, .3])
ch_idx, time_idx = _get_peak(data, times, mode='abs')
assert_equal(ch_idx, 1)
assert_equal(time_idx, 1)
ch_idx, time_idx = _get_peak(data * -1, times, mode='neg')
assert_equal(ch_idx, 0)
assert_equal(time_idx, 2)
ch_idx, time_idx = _get_peak(data, times, mode='pos')
assert_equal(ch_idx, 0)
assert_equal(time_idx, 2)
assert_raises(ValueError, _get_peak, data + 1e3, times, mode='neg')
assert_raises(ValueError, _get_peak, data - 1e3, times, mode='pos')
def test_make_field_map_eeg():
"""Test interpolation of EEG field onto head
"""
trans = read_trans(trans_fname)
evoked = read_evoked(evoked_fname, setno='Left Auditory')
evoked.info['bads'] = ['MEG 2443', 'EEG 053'] # add some bads
surf = get_head_surf('sample', subjects_dir=subjects_dir)
# we must have trans if surface is in MRI coords
assert_raises(ValueError, _make_surface_mapping, evoked.info, surf, 'eeg')
evoked = pick_types_evoked(evoked, meg=False, eeg=True)
fmd = make_field_map(evoked, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir)
# trans is necessary for EEG only
assert_raises(RuntimeError, make_field_map, evoked, trans_fname=None,
subject='sample', subjects_dir=subjects_dir)
fmd = make_field_map(evoked, trans_fname=trans_fname,
subject='sample', subjects_dir=subjects_dir)
assert_true(len(fmd) == 1)
assert_array_equal(fmd[0]['data'].shape, (2562, 59)) # maps data onto surf
assert_true(len(fmd[0]['ch_names']), 59)
def test_plot_topomap():
"""Test topomap plotting
"""
# evoked
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
evoked = fiff.read_evoked(evoked_fname, 'Left Auditory',
baseline=(None, 0))
evoked.plot_topomap(0.1, 'mag', layout=layout)
plot_evoked_topomap(evoked, None, ch_type='mag')
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type='eeg')
plot_evoked_topomap(evoked, times, ch_type='grad')
plot_evoked_topomap(evoked, times, ch_type='planar1')
plot_evoked_topomap(evoked, times, ch_type='planar2')
with warnings.catch_warnings(record=True): # delaunay triangulation warning
plot_evoked_topomap(evoked, times, ch_type='mag', layout='auto')
assert_raises(RuntimeError, plot_evoked_topomap, evoked, 0.1, 'mag',
proj='interactive') # projs have already been applied
evoked.proj = False # let's fake it like they haven't been applied
plot_evoked_topomap(evoked, 0.1, 'mag', proj='interactive')
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
projs = read_proj(ecg_fname)
projs = [p for p in projs if p['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs)
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == fiff.FIFF.FIFFV_COIL_EEG:
if ch['eeg_loc'] is not None:
ch['eeg_loc'].fill(0)
ch['loc'].fill(0)
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
times, ch_type='eeg')
def test_make_field_map_meg():
"""Test interpolation of MEG field onto helmet
"""
evoked = read_evoked(evoked_fname, setno='Left Auditory')
info = evoked.info
surf = get_meg_helmet_surf(info)
# let's reduce the number of channels by a bunch to speed it up
info['bads'] = info['ch_names'][:200]
# bad ch_type
assert_raises(ValueError, _make_surface_mapping, info, surf, 'foo')
# bad mode
assert_raises(ValueError, _make_surface_mapping, info, surf, 'meg',
mode='foo')
# no picks
evoked_eeg = pick_types_evoked(evoked, meg=False, eeg=True)
assert_raises(RuntimeError, _make_surface_mapping, evoked_eeg.info,
surf, 'meg')
# bad surface def
nn = surf['nn']
del surf['nn']
assert_raises(KeyError, _make_surface_mapping, info, surf, 'meg')
surf['nn'] = nn
cf = surf['coord_frame']
del surf['coord_frame']
assert_raises(KeyError, _make_surface_mapping, info, surf, 'meg')
surf['coord_frame'] = cf
# now do it with make_field_map
evoked = pick_types_evoked(evoked, meg=True, eeg=False)
fmd = make_field_map(evoked, trans_fname=None,
subject='sample', subjects_dir=subjects_dir)
assert_true(len(fmd) == 1)
assert_array_equal(fmd[0]['data'].shape, (304, 106)) # maps data onto surf
assert_true(len(fmd[0]['ch_names']), 106)
assert_raises(ValueError, make_field_map, evoked, ch_type='foobar')
def test_shift_time_evoked():
""" Test for shifting of time scale
"""
# Shift backward
ave = read_evoked(fname, 0)
ave.shift_time(-0.1, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
# Shift forward twice the amount
ave_bshift = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_bshift.shift_time(0.2, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave_bshift)
# Shift backward again
ave_fshift = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_fshift.shift_time(-0.1, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave_fshift)
ave_normal = read_evoked(fname, 0)
ave_relative = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
assert_true(
np.allclose(ave_normal.data, ave_relative.data, atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave_normal.times, ave_relative.times, 10)
assert_equal(ave_normal.last, ave_relative.last)
assert_equal(ave_normal.first, ave_relative.first)
# Absolute time shift
ave = read_evoked(fname, 0)
ave.shift_time(-0.3, relative=False)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave_absolute = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
assert_true(
np.allclose(ave_normal.data, ave_absolute.data, atol=1e-16, rtol=1e-3))
assert_equal(ave_absolute.first, int(-0.3 * ave.info['sfreq']))
def test_shift_time_evoked():
""" Test for shifting of time scale
"""
# Shift backward
ave = read_evoked(fname, 0)
ave.shift_time(-0.1, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
# Shift forward twice the amount
ave_bshift = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_bshift.shift_time(0.2, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave_bshift)
# Shift backward again
ave_fshift = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
ave_fshift.shift_time(-0.1, relative=True)
write_evoked(op.join(tempdir, 'evoked.fif'), ave_fshift)
ave_normal = read_evoked(fname, 0)
ave_relative = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
assert_true(np.allclose(ave_normal.data, ave_relative.data,
atol=1e-16, rtol=1e-3))
assert_array_almost_equal(ave_normal.times, ave_relative.times, 10)
assert_equal(ave_normal.last, ave_relative.last)
assert_equal(ave_normal.first, ave_relative.first)
# Absolute time shift
ave = read_evoked(fname, 0)
ave.shift_time(-0.3, relative=False)
write_evoked(op.join(tempdir, 'evoked.fif'), ave)
ave_absolute = read_evoked(op.join(tempdir, 'evoked.fif'), 0)
assert_true(np.allclose(ave_normal.data, ave_absolute.data,
atol=1e-16, rtol=1e-3))
assert_equal(ave_absolute.first, int(-0.3 * ave.info['sfreq']))
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis-meg-oct-6-fwd.fif')
label = 'Aud-rh'
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
evoked = fiff.Evoked(fname_data, setno=1, baseline=(None, 0))
# Read noise covariance matrix
cov = read_cov(fname_cov)
# Handling average file
setno = 0
loose = None
evoked = fiff.read_evoked(fname_data, setno=setno, baseline=(None, 0))
evoked.crop(tmin=0.08, tmax=0.12)
# Handling forward solution
forward = read_forward_solution(fname_fwd, force_fixed=True)
label = read_label(fname_label)
def test_MxNE_inverse():
"""Test MxNE inverse computation"""
alpha = 60 # spatial regularization parameter
stc = mixed_norm(evoked, forward, cov, alpha, loose=None, depth=0.9,
maxit=500, tol=1e-4, active_set_size=10)
assert_array_almost_equal(stc.times, evoked.times, 5)
assert_true(stc.vertno[1][0] in label.vertices)
def test_mxne_inverse():
"""Test (TF-)MxNE inverse computation"""
# Handling forward solution
evoked = fiff.Evoked(fname_data, setno=1, baseline=(None, 0))
# Read noise covariance matrix
cov = read_cov(fname_cov)
# Handling average file
setno = 0
loose = None
depth = 0.9
evoked = fiff.read_evoked(fname_data, setno=setno, baseline=(None, 0))
evoked.crop(tmin=-0.1, tmax=0.4)
evoked_l21 = copy.deepcopy(evoked)
evoked_l21.crop(tmin=0.08, tmax=0.1)
label = read_label(fname_label)
weights_min = 0.5
forward = read_forward_solution(fname_fwd,
force_fixed=False,
surf_ori=True)
# Reduce source space to make test computation faster
inverse_operator = make_inverse_operator(evoked.info,
forward,
cov,
loose=loose,
depth=depth,
fixed=True)
stc_dspm = apply_inverse(evoked_l21,
inverse_operator,
lambda2=1. / 9.,
method='dSPM')
stc_dspm.data[np.abs(stc_dspm.data) < 12] = 0.0
stc_dspm.data[np.abs(stc_dspm.data) >= 12] = 1.
# MxNE tests
alpha = 60 # spatial regularization parameter
stc_prox = mixed_norm(evoked_l21,
forward,
cov,
alpha,
loose=None,
depth=0.9,
maxit=1000,
tol=1e-8,
active_set_size=10,
solver='prox')
stc_cd = mixed_norm(evoked_l21,
forward,
cov,
alpha,
loose=None,
depth=0.9,
maxit=1000,
tol=1e-8,
active_set_size=10,
solver='cd')
assert_array_almost_equal(stc_prox.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_cd.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_prox.data, stc_cd.data, 5)
assert_true(stc_prox.vertno[1][0] in label.vertices)
assert_true(stc_cd.vertno[1][0] in label.vertices)
stc, _ = mixed_norm(evoked_l21,
forward,
cov,
alpha,
loose=None,
depth=depth,
maxit=500,
tol=1e-4,
active_set_size=10,
weights=stc_dspm,
weights_min=weights_min,
return_residual=True)
assert_array_almost_equal(stc.times, evoked_l21.times, 5)
assert_true(stc.vertno[1][0] in label.vertices)
# Do with TF-MxNE for test memory savings
alpha_space = 60. # spatial regularization parameter
alpha_time = 1. # temporal regularization parameter
stc, _ = tf_mixed_norm(evoked,
forward,
cov,
alpha_space,
alpha_time,
loose=loose,
depth=depth,
maxit=100,
tol=1e-4,
tstep=4,
wsize=16,
window=0.1,
weights=stc_dspm,
weights_min=weights_min,
return_residual=True)
assert_array_almost_equal(stc.times, evoked.times, 5)
assert_true(stc.vertno[1][0] in label.vertices)
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_fwd = op.join(data_path, 'MEG', 'sample',
'sample_audvis-meg-oct-6-fwd.fif')
label = 'Aud-rh'
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', '%s.label' % label)
evoked = fiff.Evoked(fname_data, setno=1, baseline=(None, 0))
# Read noise covariance matrix
cov = read_cov(fname_cov)
# Handling average file
setno = 0
loose = None
evoked = fiff.read_evoked(fname_data, setno=setno, baseline=(None, 0))
evoked.crop(tmin=0.08, tmax=0.12)
# Handling forward solution
forward = read_forward_solution(fname_fwd, force_fixed=True)
label = read_label(fname_label)
def test_MxNE_inverse():
"""Test MxNE inverse computation"""
alpha = 60 # spatial regularization parameter
stc = mixed_norm(evoked,
forward,
cov,
alpha,
loose=None,
# # License: BSD (3-clause) print __doc__ import os import mne from mne import fiff fname = os.environ['MNE_SAMPLE_DATASET_PATH'] fname += '/MEG/sample/sample_audvis-ave.fif' cov_fname = os.environ['MNE_SAMPLE_DATASET_PATH'] cov_fname += '/MEG/sample/sample_audvis-cov.fif' # Reading ave = fiff.read_evoked(fname, setno=0, baseline=(None, 0)) cov = mne.Covariance() cov.load(cov_fname) ave_whiten, W = cov.whiten_evoked(ave) bads = ave_whiten['info']['bads'] ind_meg_grad = fiff.pick_types(ave['info'], meg='grad', exclude=bads) ind_meg_mag = fiff.pick_types(ave['info'], meg='mag', exclude=bads) ind_eeg = fiff.pick_types(ave['info'], meg=False, eeg=True, exclude=bads) ############################################################################### # Show result import pylab as pl pl.clf() pl.subplot(3, 1, 1)
def test_mxne_inverse():
"""Test (TF-)MxNE inverse computation"""
# Handling forward solution
evoked = fiff.Evoked(fname_data, setno=1, baseline=(None, 0))
# Read noise covariance matrix
cov = read_cov(fname_cov)
# Handling average file
setno = 0
loose = None
depth = 0.9
evoked = fiff.read_evoked(fname_data, setno=setno, baseline=(None, 0))
evoked.crop(tmin=-0.1, tmax=0.4)
evoked_l21 = copy.deepcopy(evoked)
evoked_l21.crop(tmin=0.08, tmax=0.1)
label = read_label(fname_label)
weights_min = 0.5
forward = read_forward_solution(fname_fwd, force_fixed=False,
surf_ori=True)
# Reduce source space to make test computation faster
inverse_operator = make_inverse_operator(evoked.info, forward, cov,
loose=loose, depth=depth,
fixed=True)
stc_dspm = apply_inverse(evoked_l21, inverse_operator, lambda2=1. / 9.,
method='dSPM')
stc_dspm.data[np.abs(stc_dspm.data) < 12] = 0.0
stc_dspm.data[np.abs(stc_dspm.data) >= 12] = 1.
# MxNE tests
alpha = 60 # spatial regularization parameter
stc_prox = mixed_norm(evoked_l21, forward, cov, alpha, loose=None,
depth=0.9, maxit=1000, tol=1e-8, active_set_size=10,
solver='prox')
stc_cd = mixed_norm(evoked_l21, forward, cov, alpha, loose=None,
depth=0.9, maxit=1000, tol=1e-8, active_set_size=10,
solver='cd')
assert_array_almost_equal(stc_prox.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_cd.times, evoked_l21.times, 5)
assert_array_almost_equal(stc_prox.data, stc_cd.data, 5)
assert_true(stc_prox.vertno[1][0] in label.vertices)
assert_true(stc_cd.vertno[1][0] in label.vertices)
stc, _ = mixed_norm(evoked_l21, forward, cov, alpha, loose=None,
depth=depth, maxit=500, tol=1e-4, active_set_size=10,
weights=stc_dspm, weights_min=weights_min,
return_residual=True)
assert_array_almost_equal(stc.times, evoked_l21.times, 5)
assert_true(stc.vertno[1][0] in label.vertices)
# Do with TF-MxNE for test memory savings
alpha_space = 60. # spatial regularization parameter
alpha_time = 1. # temporal regularization parameter
stc, _ = tf_mixed_norm(evoked, forward, cov, alpha_space, alpha_time,
loose=loose, depth=depth, maxit=100, tol=1e-4,
tstep=4, wsize=16, window=0.1, weights=stc_dspm,
weights_min=weights_min, return_residual=True)
assert_array_almost_equal(stc.times, evoked.times, 5)
assert_true(stc.vertno[1][0] in label.vertices)
from mne import fiff from mne.datasets import sample from mne.inverse_sparse import mixed_norm from mne.minimum_norm import make_inverse_operator, apply_inverse from mne.viz import plot_sparse_source_estimates data_path = sample.data_path() fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' ave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif' cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif' # Read noise covariance matrix cov = mne.read_cov(cov_fname) # Handling average file setno = 0 evoked = fiff.read_evoked(ave_fname, setno=setno, baseline=(None, 0)) evoked.crop(tmin=0, tmax=0.3) # Handling forward solution forward = mne.read_forward_solution(fwd_fname, surf_ori=True) cov = mne.cov.regularize(cov, evoked.info) import matplotlib.pyplot as plt plt.figure() ylim = dict(eeg=[-10, 10], grad=[-400, 400], mag=[-600, 600]) evoked.plot(ylim=ylim, proj=True) ############################################################################### # Run solver alpha = 70 # regularization parameter between 0 and 100 (100 is high) loose, depth = 0.2, 0.9 # loose orientation & depth weighting
def test_plot_topomap():
"""Test topomap plotting
"""
# evoked
warnings.simplefilter('always', UserWarning)
with warnings.catch_warnings(record=True):
evoked = fiff.read_evoked(evoked_fname,
'Left Auditory',
baseline=(None, 0))
evoked.plot_topomap(0.1, 'mag', layout=layout)
plot_evoked_topomap(evoked, None, ch_type='mag')
times = [0.1, 0.2]
plot_evoked_topomap(evoked, times, ch_type='eeg')
plot_evoked_topomap(evoked, times, ch_type='grad')
plot_evoked_topomap(evoked, times, ch_type='planar1')
plot_evoked_topomap(evoked, times, ch_type='planar2')
plot_evoked_topomap(evoked, times, ch_type='grad', show_names=True)
p = plot_evoked_topomap(evoked,
times,
ch_type='grad',
show_names=lambda x: x.replace('MEG', ''))
subplot = [
x for x in p.get_children()
if isinstance(x, matplotlib.axes.Subplot)
][0]
assert_true(
all('MEG' not in x.get_text() for x in subplot.get_children()
if isinstance(x, matplotlib.text.Text)))
# Test title
def get_texts(p):
return [
x.get_text() for x in p.get_children()
if isinstance(x, matplotlib.text.Text)
]
p = plot_evoked_topomap(evoked, times, ch_type='eeg')
assert_equal(len(get_texts(p)), 0)
p = plot_evoked_topomap(evoked, times, ch_type='eeg', title='Custom')
texts = get_texts(p)
assert_equal(len(texts), 1)
assert_equal(texts[0], 'Custom')
# delaunay triangulation warning
with warnings.catch_warnings(record=True):
plot_evoked_topomap(evoked, times, ch_type='mag', layout='auto')
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
0.1,
'mag',
proj='interactive') # projs have already been applied
evoked.proj = False # let's fake it like they haven't been applied
plot_evoked_topomap(evoked, 0.1, 'mag', proj='interactive')
assert_raises(RuntimeError, plot_evoked_topomap, evoked,
np.repeat(.1, 50))
assert_raises(ValueError, plot_evoked_topomap, evoked, [-3e12, 15e6])
projs = read_proj(ecg_fname)
projs = [p for p in projs if p['desc'].lower().find('eeg') < 0]
plot_projs_topomap(projs)
plt.close('all')
for ch in evoked.info['chs']:
if ch['coil_type'] == fiff.FIFF.FIFFV_COIL_EEG:
if ch['eeg_loc'] is not None:
ch['eeg_loc'].fill(0)
ch['loc'].fill(0)
assert_raises(RuntimeError,
plot_evoked_topomap,
evoked,
times,
ch_type='eeg')
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse import tf_mixed_norm
from mne.viz import plot_sparse_source_estimates
data_path = sample.data_path()
fwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = data_path + '/MEG/sample/sample_audvis-no-filter-ave.fif'
cov_fname = data_path + '/MEG/sample/sample_audvis-cov.fif'
# Read noise covariance matrix
cov = mne.read_cov(cov_fname)
# Handling average file
setno = 'Left visual'
evoked = fiff.read_evoked(ave_fname, setno=setno, baseline=(None, 0))
evoked = fiff.pick.pick_channels_evoked(evoked)
# We make the window slightly larger than what you'll eventually be interested
# in ([-0.05, 0.3]) to avoid edge effects.
evoked.crop(tmin=-0.1, tmax=0.4)
# Handling forward solution
forward = mne.read_forward_solution(fwd_fname, force_fixed=False,
surf_ori=True)
cov = mne.cov.regularize(cov, evoked.info)
###############################################################################
# Run solver
# alpha_space regularization parameter is between 0 and 100 (100 is high)
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print __doc__
import pylab as pl
from mne import fiff
from mne.layouts import Layout
from mne.viz import plot_topo
from mne.datasets import sample
data_path = sample.data_path('.')
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
evoked = fiff.read_evoked(fname, setno=0, baseline=(None, 0))
layout = Layout('Vectorview-all')
###############################################################################
# Show topography
plot_topo(evoked, layout)
title = 'MNE sample data (condition : %s)' % evoked.comment
pl.figtext(0.03, 0.93, title, color='w', fontsize=18)
pl.show()