def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space"""
evoked = fiff.Evoked(fname_data, setno=0, baseline=(None, 0))
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
stc.save('tmp-vl.stc')
stc2 = read_source_estimate('tmp-vl.stc')
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space"""
evoked = fiff.Evoked(fname_data, setno=0, baseline=(None, 0))
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
stc.save('tmp-vl.stc')
stc2 = read_source_estimate('tmp-vl.stc')
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space
"""
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
_compare_io(inverse_operator_vol)
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space
"""
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
_compare_io(inverse_operator_vol)
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
# volume inverses don't have associated subject IDs
assert_true(stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space
"""
evoked = _get_evoked()
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
_compare_io(inverse_operator_vol)
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
assert_true(isinstance(stc, VolSourceEstimate))
# volume inverses don't have associated subject IDs
assert_true(stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume():
"""Test MNE inverse computation on volume source space
"""
tempdir = _TempDir()
evoked = _get_evoked()
inverse_operator_vol = read_inverse_operator(fname_vol_inv)
assert_true(repr(inverse_operator_vol))
stc = apply_inverse(evoked, inverse_operator_vol, lambda2, "dSPM")
assert_true(isinstance(stc, VolSourceEstimate))
# volume inverses don't have associated subject IDs
assert_true(stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert_true(np.all(stc.data > 0))
assert_true(np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
def test_inverse_operator_volume(evoked):
"""Test MNE inverse computation on volume source space."""
tempdir = _TempDir()
inv_vol = read_inverse_operator(fname_vol_inv)
assert (repr(inv_vol))
stc = apply_inverse(evoked, inv_vol, lambda2, 'dSPM')
assert (isinstance(stc, VolSourceEstimate))
# volume inverses don't have associated subject IDs
assert (stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert (np.all(stc.data > 0))
assert (np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
# vector source estimate
stc_vec = apply_inverse(evoked, inv_vol, lambda2, 'dSPM', 'vector')
assert (repr(stc_vec))
assert_allclose(np.linalg.norm(stc_vec.data, axis=1), stc.data)
def test_inverse_operator_volume(evoked):
"""Test MNE inverse computation on volume source space."""
tempdir = _TempDir()
inv_vol = read_inverse_operator(fname_vol_inv)
assert (repr(inv_vol))
stc = apply_inverse(evoked, inv_vol, lambda2, 'dSPM')
assert (isinstance(stc, VolSourceEstimate))
# volume inverses don't have associated subject IDs
assert (stc.subject is None)
stc.save(op.join(tempdir, 'tmp-vl.stc'))
stc2 = read_source_estimate(op.join(tempdir, 'tmp-vl.stc'))
assert (np.all(stc.data > 0))
assert (np.all(stc.data < 35))
assert_array_almost_equal(stc.data, stc2.data)
assert_array_almost_equal(stc.times, stc2.times)
# vector source estimate
stc_vec = apply_inverse(evoked, inv_vol, lambda2, 'dSPM', 'vector')
assert (repr(stc_vec))
assert_allclose(np.linalg.norm(stc_vec.data, axis=1), stc.data)
return smooth_mat_lh.tocsc() + smooth_mat_rh.tocsc()
smoothing_matrix = read_smoothing_matrix()
def threshold_a_colormap(colormap, lower_threshold, upper_threshold):
sample_points = np.linspace(0.0, 1.0, 256)
color_list = colormap(sample_points)
invisible_idx = np.logical_and(
lower_threshold <= sample_points,
sample_points <= upper_threshold)
color_list[invisible_idx, -1] = 0 # The last number is opacity
name = '{}_thresholded'.format(colormap.name)
return mpl_colors.LinearSegmentedColormap.from_list(name=name, colors=color_list)
data = read_source_estimate('playground/vs_pysurfer/psi_stc-lh.stc').data[:, 0]
threshold = 90
pcts = [(100 - threshold)/2, (100 + threshold)/2]
pctls = np.percentile(data, pcts)
self = painter
painter.colormap_mode = 'local'
data_smoothed = smoothing_matrix.dot(data)
painter.vmin, painter.vmax = np.min(data), np.max(data)
sources_normalized = painter.normalize_to_01(data_smoothed)
invisible_idx = np.where((data_smoothed >= pctls[0]) & (data_smoothed <= pctls[1]))