def test_simulate_NIRS():
raw = simulate_nirs_raw(sfreq=3.,
amplitude=1.,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
assert 'hbo' in raw
assert raw.info['sfreq'] == 3.
assert raw.get_data().shape == (1, 900)
assert np.max(raw.get_data()) < 1.2 * 1.e-6
assert raw.annotations.description[0] == 'A'
assert raw.annotations.duration[0] == 5
assert np.min(np.diff(raw.annotations.onset)) > 15. + 5.
assert np.max(np.diff(raw.annotations.onset)) < 45. + 5.
with pytest.raises(AssertionError, match='Same number of'):
raw = simulate_nirs_raw(sfreq=3.,
amplitude=[1., 2.],
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
raw = simulate_nirs_raw(sfreq=3.,
amplitude=[0., 2., 4.],
annot_desc=['Control', 'Cond_A', 'Cond_B'],
stim_dur=[5, 5, 5],
sig_dur=900.,
isi_min=15.,
isi_max=45.)
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.0,
drift_order=1,
drift_model='polynomial')
glm_est = run_GLM(raw, design_matrix)
df = glm_to_tidy(raw, glm_est, design_matrix)
df = _tidy_long_to_wide(df)
assert df.query("condition in ['Control']")['theta'].values[0] == \
pytest.approx(0)
assert df.query("condition in ['Cond_A']")['theta'].values[0] == \
pytest.approx(2e-6)
assert df.query("condition in ['Cond_B']")['theta'].values[0] == \
pytest.approx(4e-6)
def test_run_GLM_order():
raw = simulate_nirs_raw(sig_dur=200, stim_dur=5., sfreq=3)
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.,
drift_order=1,
drift_model='polynomial')
# Default should be first order AR
glm_estimates = run_glm(raw, design_matrix)
assert glm_estimates.pick("Simulated").model()[0].order == 1
# Default should be first order AR
glm_estimates = run_glm(raw, design_matrix, noise_model='ar2')
assert glm_estimates.pick("Simulated").model()[0].order == 2
glm_estimates = run_glm(raw, design_matrix, noise_model='ar7')
assert glm_estimates.pick("Simulated").model()[0].order == 7
# Auto should be 4 times sample rate
cov = Covariance(np.ones(1) * 1e-11,
raw.ch_names,
raw.info['bads'],
raw.info['projs'],
nfree=0)
raw = add_noise(raw, cov, iir_filter=iir_filter)
glm_estimates = run_glm(raw, design_matrix, noise_model='auto')
assert glm_estimates.pick("Simulated").model()[0].order == 3 * 4
raw = simulate_nirs_raw(sig_dur=10, stim_dur=5., sfreq=2)
cov = Covariance(np.ones(1) * 1e-11,
raw.ch_names,
raw.info['bads'],
raw.info['projs'],
nfree=0)
raw = add_noise(raw, cov, iir_filter=iir_filter)
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.,
drift_order=1,
drift_model='polynomial')
# Auto should be 4 times sample rate
glm_estimates = run_glm(raw, design_matrix, noise_model='auto')
assert glm_estimates.pick("Simulated").model()[0].order == 2 * 4
def test_high_pass_helpers():
# Test the helpers give reasonable values
raw = simulate_nirs_raw(sfreq=1.,
amplitude=1.,
sig_dur=300.,
stim_dur=1.,
isi_min=20.,
isi_max=38.)
lisi, names = longest_inter_annotation_interval(raw)
lisi = lisi[0]
assert lisi >= 20
assert lisi <= 40
assert drift_high_pass(raw) >= 1 / (40 * 2)
assert drift_high_pass(raw) <= 1 / (20 * 2)
def test_simulate_NIRS_single_channel():
raw = simulate_nirs_raw(sfreq=3.,
amplitude=1.,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
assert 'hbo' in raw
assert raw.info['sfreq'] == 3.
assert raw.get_data().shape == (1, 900)
assert np.max(raw.get_data()) < 1.2 * 1.e-6
assert raw.annotations.description[0] == 'A'
assert raw.annotations.duration[0] == 5
assert np.min(np.diff(raw.annotations.onset)) > 15. + 5.
assert np.max(np.diff(raw.annotations.onset)) < 45. + 5.
with pytest.raises(AssertionError, match='Same number of'):
_ = simulate_nirs_raw(sfreq=3.,
amplitude=[1., 2.],
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
def test_simulate_NIRS():
raw = simulate_nirs_raw(sfreq=3.,
amplitude=1.,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
assert 'hbo' in raw
assert raw.info['sfreq'] == 3.
assert raw.get_data().shape == (1, 900)
assert np.max(raw.get_data()) < 1.2 * 1.e-6
assert raw.annotations.description[0] == 'A'
assert raw.annotations.duration[0] == 5
assert np.min(np.diff(raw.annotations.onset)) > 15. + 5.
assert np.max(np.diff(raw.annotations.onset)) < 45. + 5.
def test_run_GLM():
raw = simulate_nirs_raw(sig_dur=200, stim_dur=5.)
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.,
drift_order=1,
drift_model='polynomial')
glm_estimates = run_GLM(raw, design_matrix)
assert len(glm_estimates) == len(raw.ch_names)
# Check the estimate is correct within 10% error
assert abs(glm_estimates["Simulated"].theta[0] - 1.e-6) < 0.1e-6
# ensure we return the same type as nilearn to encourage compatibility
_, ni_est = nilearn.glm.first_level.run_glm(
raw.get_data(0).T, design_matrix.values)
assert type(ni_est) == type(glm_estimates)
def test_cropped_raw():
# Ensure timing is correct for cropped signals
raw = simulate_nirs_raw(sfreq=1.,
amplitude=1.,
sig_dur=300.,
stim_dur=1.,
isi_min=20.,
isi_max=40.)
onsets = raw.annotations.onset
onsets_after_crop = [onsets[idx] for idx in np.where(onsets > 100)]
raw.crop(tmin=100)
design_matrix = make_first_level_design_matrix(raw,
drift_order=0,
drift_model='polynomial')
# 100 corrects for the crop time above
# 4 is peak time after onset
new_idx = np.round(onsets_after_crop[0][0]) - 100 + 4
assert design_matrix["A"][new_idx] > 0.1
def test_simulate_NIRS_multi_channel():
raw = simulate_nirs_raw(sfreq=3.,
amplitude=[0., 2., 4.],
annot_desc=['Control', 'Cond_A', 'Cond_B'],
stim_dur=[5, 5, 5],
sig_dur=1500.,
isi_min=5.,
isi_max=15.,
hrf_model='spm')
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.0,
drift_order=0,
drift_model='polynomial')
assert len(design_matrix['Control']) == 1500 * 3
assert len(design_matrix['Cond_A']) == 1500 * 3
# Make sure no extra channels. Specifically the default isn't present.
with pytest.raises(KeyError, match='A'):
len(design_matrix['A'])
def test_statsmodel_to_df(func):
func = getattr(smf, func)
np.random.seed(0)
amplitude = 1.432
df_cha = pd.DataFrame()
for n in range(5):
raw = simulate_nirs_raw(sfreq=3.,
amplitude=amplitude,
sig_dur=300.,
stim_dur=5.,
isi_min=15.,
isi_max=45.)
raw._data += np.random.normal(0, np.sqrt(1e-12), raw._data.shape)
design_matrix = make_first_level_design_matrix(raw, stim_dur=5.0)
glm_est = run_glm(raw, design_matrix)
with pytest.warns(RuntimeWarning, match='Non standard source detect'):
cha = glm_est.to_dataframe()
cha["ID"] = '%02d' % n
df_cha = pd.concat([df_cha, cha], ignore_index=True)
df_cha["theta"] = df_cha["theta"] * 1.0e6
roi_model = func("theta ~ -1 + Condition", df_cha,
groups=df_cha["ID"]).fit()
df = statsmodels_to_results(roi_model)
assert type(df) == pd.DataFrame
assert_allclose(df["Coef."]["Condition[A]"], amplitude, rtol=0.1)
assert df["Significant"]["Condition[A]"]
assert df.shape == (8, 8)
roi_model = smf.rlm("theta ~ -1 + Condition", df_cha,
groups=df_cha["ID"]).fit()
df = statsmodels_to_results(roi_model)
assert type(df) == pd.DataFrame
assert_allclose(df["Coef."]["Condition[A]"], amplitude, rtol=0.1)
assert df["Significant"]["Condition[A]"]
assert df.shape == (8, 8)
def test_run_GLM():
raw = simulate_nirs_raw(sig_dur=200, stim_dur=5.)
design_matrix = make_first_level_design_matrix(raw,
stim_dur=5.,
drift_order=1,
drift_model='polynomial')
glm_estimates = run_glm(raw, design_matrix)
# Test backwards compatibility
with pytest.deprecated_call(match='more comprehensive'):
old_res = run_GLM(raw, design_matrix)
assert old_res.keys() == glm_estimates.data.keys()
assert (old_res["Simulated"].theta == glm_estimates.data["Simulated"].theta
).all()
assert len(glm_estimates) == len(raw.ch_names)
# Check the estimate is correct within 10% error
assert abs(glm_estimates.pick("Simulated").theta()[0][0] - 1.e-6) < 0.1e-6
# ensure we return the same type as nilearn to encourage compatibility
_, ni_est = nilearn.glm.first_level.run_glm(
raw.get_data(0).T, design_matrix.values)
assert isinstance(glm_estimates._data, type(ni_est))
# Here we look at the effect of the interstimulus interval on the
# expected haemodynamic response. We choose a few different
# maximum and minimum
# values for the ISI. Two repeats are plotted per
# ISI to illustrate the random selection.
# Some common high pass filter values from literature are shown in red.
sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=60))
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 10))
for rep in range(2):
for column, min_isi in enumerate([0, 15]):
for row, max_isi in enumerate([15, 30, 45, 60]):
if max_isi >= min_isi:
raw = simulate_nirs_raw(sfreq=4.,
sig_dur=60 * 60,
amplitude=1.,
stim_dur=5.,
isi_min=min_isi,
isi_max=max_isi)
raw._data[0] = raw._data[0] - np.mean(raw._data[0])
raw.pick(picks='hbo').plot_psd(average=True,
fmax=2,
ax=axes[rep, column],
show=False,
color=sm.cmap(sm.norm(max_isi)),
xscale='log')
axes[rep, column].set_ylim(-60, 20)
axes[rep, column].set_title(
'ISI: {} (s) to Max ISI'.format(min_isi))
for filt in [0.01, 0.02, 0.05]:
axes[rep, column].axvline(x=filt,
linestyle=":",
