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)
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]))