def test_run_plot_GLM_topo():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
glm_estimates = run_glm(raw_haemo, design_matrix)
fig = plot_glm_topo(raw_haemo, glm_estimates.data, design_matrix)
# 5 conditions (A,B,C,Drift,Constant) * two chroma + 2xcolorbar
assert len(fig.axes) == 12
# Two conditions * two chroma + 2 x colorbar
fig = plot_glm_topo(raw_haemo,
glm_estimates.data,
design_matrix,
requested_conditions=['A', 'B'])
assert len(fig.axes) == 6
# Two conditions * one chroma + 1 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
requested_conditions=['A', 'B'])
assert len(fig.axes) == 3
# One conditions * two chroma + 2 x colorbar
fig = plot_glm_topo(raw_haemo,
glm_estimates.data,
design_matrix,
requested_conditions=['A'])
assert len(fig.axes) == 4
# One conditions * one chroma + 1 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
requested_conditions=['A'])
assert len(fig.axes) == 2
# One conditions * one chroma + 0 x colorbar
with pytest.warns(RuntimeWarning, match='Reducing GLM results'):
fig = plot_glm_topo(raw_haemo.copy().pick(picks="hbo"),
glm_estimates.data,
design_matrix,
colorbar=False,
requested_conditions=['A'])
assert len(fig.axes) == 1
# Ensure warning thrown if glm estimates is missing channels from raw
glm_estimates_subset = {
a: glm_estimates.data[a]
for a in raw_haemo.ch_names[0:3]
}
with pytest.raises(RuntimeError, match="does not match regression"):
plot_glm_topo(raw_haemo, glm_estimates_subset, design_matrix)
def test_run_plot_GLM_contrast_topo():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
glm_est = run_GLM(raw_haemo, design_matrix)
contrast_matrix = np.eye(design_matrix.shape[1])
basic_conts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrast_LvR = basic_conts['A'] - basic_conts['B']
contrast = mne_nirs.statistics.compute_contrast(glm_est, contrast_LvR)
fig = mne_nirs.visualisation.plot_glm_contrast_topo(raw_haemo, contrast)
assert len(fig.axes) == 3
def test_run_plot_GLM_topo():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
glm_estimates = run_GLM(raw_haemo, design_matrix)
fig = plot_glm_topo(raw_haemo, glm_estimates, design_matrix)
# 5 conditions (A,B,C,Drift,Constant) * two chroma + 2xcolorbar
assert len(fig.axes) == 12
fig = plot_glm_topo(raw_haemo, glm_estimates, design_matrix,
requested_conditions=['A', 'B'])
# Two conditions * two chroma + 2xcolorbar
assert len(fig.axes) == 6
def test_run_plot_GLM_contrast_topo_single_chroma():
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
raw_haemo = raw_haemo.pick(picks='hbo')
glm_est = run_glm(raw_haemo, design_matrix)
contrast_matrix = np.eye(design_matrix.shape[1])
basic_conts = dict([(column, contrast_matrix[i])
for i, column in enumerate(design_matrix.columns)])
contrast_LvR = basic_conts['A'] - basic_conts['B']
with pytest.deprecated_call(match='comprehensive GLM'):
contrast = mne_nirs.statistics.compute_contrast(
glm_est.data, contrast_LvR)
with pytest.deprecated_call(match='comprehensive GLM'):
fig = mne_nirs.visualisation.plot_glm_contrast_topo(
raw_haemo, contrast)
assert len(fig.axes) == 2
def test_run_plot_GLM_projection(requires_pyvista):
raw_intensity = _load_dataset()
raw_intensity.crop(450, 600) # Keep the test fast
design_matrix = make_first_level_design_matrix(raw_intensity,
drift_order=1,
drift_model='polynomial')
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
glm_estimates = run_glm(raw_haemo, design_matrix)
df = glm_to_tidy(raw_haemo, glm_estimates.data, design_matrix)
df = df.query("Chroma in 'hbo'")
df = df.query("Condition in 'A'")
brain = plot_glm_surface_projection(raw_haemo.copy().pick("hbo"),
df,
clim='auto',
view='dorsal',
colorbar=True,
size=(800, 700),
value="theta",
surface='white',
subjects_dir=subjects_dir)
assert type(brain) == mne.viz._brain.Brain
