def test_dics():
"""Test DICS with evoked data and single trials
"""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
stc = dics(evoked, forward, noise_csd=noise_csd, data_csd=data_csd,
label=label)
stc.crop(0, None)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
# Incorrect due to limited number of epochs
assert_true(0.04 < tmax < 0.05)
assert_true(10 < np.max(max_stc) < 13)
# Test picking normal orientation
stc_normal = dics(evoked, forward_surf_ori, noise_csd, data_csd,
pick_ori="normal", label=label)
stc_normal.crop(0, None)
# The amplitude of normal orientation results should always be smaller than
# free orientation results
assert_true((np.abs(stc_normal.data) <= stc.data).all())
# Test if fixed forward operator is detected when picking normal
# orientation
assert_raises(ValueError, dics_epochs, epochs, forward_fixed, noise_csd,
data_csd, pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
assert_raises(ValueError, dics_epochs, epochs, forward, noise_csd,
data_csd, pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
assert_raises(ValueError, dics_epochs, epochs, forward_vol, noise_csd,
data_csd, pick_ori="normal")
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
stcs = dics_epochs(epochs, forward_fixed, noise_csd, data_csd, reg=0.01,
label=label)
# Testing returning of generator
stcs_ = dics_epochs(epochs, forward_fixed, noise_csd, data_csd, reg=0.01,
return_generator=True, label=label)
assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
# Test whether correct number of trials was returned
epochs.drop_bad()
assert_true(len(epochs.events) == len(stcs))
# Average the single trial estimates
stc_avg = np.zeros_like(stc.data)
for this_stc in stcs:
stc_avg += this_stc.crop(0, None).data
stc_avg /= len(stcs)
idx = np.argmax(np.max(stc_avg, axis=1))
max_stc = stc_avg[idx]
tmax = stc.times[np.argmax(max_stc)]
assert_true(0.045 < tmax < 0.06) # incorrect due to limited # of epochs
assert_true(12 < np.max(max_stc) < 18.5)
def test_dics():
"""Test DICS with evoked data and single trials."""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
epochs.crop(0, None)
reg = 0.5 # Heavily regularize due to low SNR
for real_filter in (True, False):
stc = dics(evoked, forward, noise_csd=noise_csd, data_csd=data_csd,
label=label, real_filter=real_filter, reg=reg)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
# Incorrect due to limited number of epochs
assert 0.04 < tmax < 0.06
assert 3. < np.max(max_stc) < 6.
# Test picking normal orientation
stc_normal = dics(evoked, forward_surf_ori, noise_csd, data_csd,
pick_ori="normal", label=label, real_filter=True,
reg=reg)
assert stc_normal.data.min() < 0 # this doesn't take abs
stc_normal = dics(evoked, forward_surf_ori, noise_csd, data_csd,
pick_ori="normal", label=label, reg=reg)
assert stc_normal.data.min() >= 0 # this does take abs
# The amplitude of normal orientation results should always be smaller than
# free orientation results
assert (np.abs(stc_normal.data) <= stc.data).all()
# Test if fixed forward operator is detected when picking normal
# orientation
raises(ValueError, dics_epochs, epochs, forward_fixed, noise_csd, data_csd,
pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
raises(ValueError, dics_epochs, epochs, forward, noise_csd, data_csd,
pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
raises(ValueError, dics_epochs, epochs, forward_vol, noise_csd, data_csd,
pick_ori="normal")
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
stcs = dics_epochs(epochs, forward_fixed, noise_csd, data_csd, label=label)
# Testing returning of generator
stcs_ = dics_epochs(epochs, forward_fixed, noise_csd, data_csd,
return_generator=True, label=label)
assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
# Test whether correct number of trials was returned
epochs.drop_bad()
assert len(epochs.events) == len(stcs)
# Average the single trial estimates
stc_avg = np.zeros_like(stc.data)
for this_stc in stcs:
stc_avg += this_stc.data
stc_avg /= len(stcs)
idx = np.argmax(np.max(stc_avg, axis=1))
max_stc = stc_avg[idx]
tmax = stc.times[np.argmax(max_stc)]
assert 0.120 < tmax < 0.150 # incorrect due to limited #
assert 12 < np.max(max_stc) < 18.5
def test_dics():
"""Test DICS with evoked data and single trials
"""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
stc = dics(evoked,
forward,
noise_csd=noise_csd,
data_csd=data_csd,
label=label)
stc.crop(0, None)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
# Incorrect due to limited number of epochs
assert_true(0.04 < tmax < 0.05)
assert_true(10 < np.max(max_stc) < 13)
# Test picking normal orientation
stc_normal = dics(evoked,
forward_surf_ori,
noise_csd,
data_csd,
pick_ori="normal",
label=label)
stc_normal.crop(0, None)
# The amplitude of normal orientation results should always be smaller than
# free orientation results
assert_true((np.abs(stc_normal.data) <= stc.data).all())
# Test if fixed forward operator is detected when picking normal
# orientation
assert_raises(ValueError,
dics_epochs,
epochs,
forward_fixed,
noise_csd,
data_csd,
pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
assert_raises(ValueError,
dics_epochs,
epochs,
forward,
noise_csd,
data_csd,
pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
assert_raises(ValueError,
dics_epochs,
epochs,
forward_vol,
noise_csd,
data_csd,
pick_ori="normal")
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
stcs = dics_epochs(epochs,
forward_fixed,
noise_csd,
data_csd,
reg=0.01,
label=label)
# Testing returning of generator
stcs_ = dics_epochs(epochs,
forward_fixed,
noise_csd,
data_csd,
reg=0.01,
return_generator=True,
label=label)
assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
# Test whether correct number of trials was returned
epochs.drop_bad_epochs()
assert_true(len(epochs.events) == len(stcs))
# Average the single trial estimates
stc_avg = np.zeros_like(stc.data)
for this_stc in stcs:
stc_avg += this_stc.crop(0, None).data
stc_avg /= len(stcs)
idx = np.argmax(np.max(stc_avg, axis=1))
max_stc = stc_avg[idx]
tmax = stc.times[np.argmax(max_stc)]
assert_true(0.045 < tmax < 0.06) # incorrect due to limited # of epochs
assert_true(12 < np.max(max_stc) < 18.5)
def test_dics():
"""Test DICS with evoked data and single trials."""
raw, epochs, evoked, data_csd, noise_csd, label, forward,\
forward_surf_ori, forward_fixed, forward_vol = _get_data()
epochs.crop(0, None)
reg = 0.5 # Heavily regularize due to low SNR
for real_filter in (True, False):
stc = dics(evoked,
forward,
noise_csd=noise_csd,
data_csd=data_csd,
label=label,
real_filter=real_filter,
reg=reg)
stc_pow = np.sum(stc.data, axis=1)
idx = np.argmax(stc_pow)
max_stc = stc.data[idx]
tmax = stc.times[np.argmax(max_stc)]
# Incorrect due to limited number of epochs
assert 0.04 < tmax < 0.06
assert 3. < np.max(max_stc) < 6.
# Test picking normal orientation
stc_normal = dics(evoked,
forward_surf_ori,
noise_csd,
data_csd,
pick_ori="normal",
label=label,
real_filter=True,
reg=reg)
assert stc_normal.data.min() < 0 # this doesn't take abs
stc_normal = dics(evoked,
forward_surf_ori,
noise_csd,
data_csd,
pick_ori="normal",
label=label,
reg=reg)
assert stc_normal.data.min() >= 0 # this does take abs
# The amplitude of normal orientation results should always be smaller than
# free orientation results
assert (np.abs(stc_normal.data) <= stc.data).all()
# Test if fixed forward operator is detected when picking normal
# orientation
raises(ValueError,
dics_epochs,
epochs,
forward_fixed,
noise_csd,
data_csd,
pick_ori="normal")
# Test if non-surface oriented forward operator is detected when picking
# normal orientation
raises(ValueError,
dics_epochs,
epochs,
forward,
noise_csd,
data_csd,
pick_ori="normal")
# Test if volume forward operator is detected when picking normal
# orientation
raises(ValueError,
dics_epochs,
epochs,
forward_vol,
noise_csd,
data_csd,
pick_ori="normal")
# Now test single trial using fixed orientation forward solution
# so we can compare it to the evoked solution
stcs = dics_epochs(epochs, forward_fixed, noise_csd, data_csd, label=label)
# Testing returning of generator
stcs_ = dics_epochs(epochs,
forward_fixed,
noise_csd,
data_csd,
return_generator=True,
label=label)
assert_array_equal(stcs[0].data, advance_iterator(stcs_).data)
# Test whether correct number of trials was returned
epochs.drop_bad()
assert len(epochs.events) == len(stcs)
# Average the single trial estimates
stc_avg = np.zeros_like(stc.data)
for this_stc in stcs:
stc_avg += this_stc.data
stc_avg /= len(stcs)
idx = np.argmax(np.max(stc_avg, axis=1))
max_stc = stc_avg[idx]
tmax = stc.times[np.argmax(max_stc)]
assert 0.120 < tmax < 0.150 # incorrect due to limited #
assert 12 < np.max(max_stc) < 18.5
