def test_io():
num_chans = 6
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
raw_intensity.resample(0.2)
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
raw_haemo = mne_nirs.channels.get_long_channels(raw_haemo)
raw_haemo.pick(picks=range(num_chans))
design_matrix = make_first_level_design_matrix(raw_intensity,
hrf_model='spm',
stim_dur=5.0,
drift_order=3,
drift_model='polynomial')
glm_est = run_GLM(raw_haemo, design_matrix)
df = glm_to_tidy(raw_haemo, glm_est, design_matrix)
df = _tidy_long_to_wide(df)
assert df.shape == (48, 11)
assert set(df.columns) == {
'ch_name', 'condition', 'df', 'mse', 'p_value', 't', 'theta', 'Source',
'Detector', 'Chroma', 'Significant'
}
num_conds = 8 # triggers (1, 2, 3, 15) + 3 drifts + constant
assert df.shape[0] == num_chans * num_conds
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['2.0'] - basic_conts['3.0']
contrast = mne_nirs.statistics.compute_contrast(glm_est, contrast_LvR)
df = glm_to_tidy(raw_haemo, contrast, design_matrix)
df = _tidy_long_to_wide(df)
assert df.shape == (6, 10)
assert set(df.columns) == {
'ch_name', 'ContrastType', 'z_score', 'stat', 'p_value', 'effect',
'Source', 'Detector', 'Chroma', 'Significant'
}
contrast = mne_nirs.statistics.compute_contrast(glm_est,
contrast_LvR,
contrast_type='F')
df = glm_to_tidy(raw_haemo, contrast, design_matrix)
df = _tidy_long_to_wide(df)
assert df.shape == (6, 10)
assert set(df.columns) == {
'ch_name', 'ContrastType', 'z_score', 'stat', 'p_value', 'effect',
'Source', 'Detector', 'Chroma', 'Significant'
}
def test_GLM_system_test():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
raw_intensity.resample(1.0)
new_des = [des for des in raw_intensity.annotations.description]
new_des = ['Control' if x == "1.0" else x for x in new_des]
new_des = ['Tapping/Left' if x == "2.0" else x for x in new_des]
new_des = ['Tapping/Right' if x == "3.0" else x for x in new_des]
annot = mne.Annotations(raw_intensity.annotations.onset,
raw_intensity.annotations.duration, new_des)
raw_intensity.set_annotations(annot)
raw_intensity.annotations.crop(35, 2967)
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od)
short_chs = get_short_channels(raw_haemo)
raw_haemo = get_long_channels(raw_haemo)
design_matrix = make_first_level_design_matrix(raw_intensity,
hrf_model='spm',
stim_dur=5.0,
drift_order=3,
drift_model='polynomial')
design_matrix["ShortHbO"] = np.mean(
short_chs.copy().pick(picks="hbo").get_data(), axis=0)
design_matrix["ShortHbR"] = np.mean(
short_chs.copy().pick(picks="hbr").get_data(), axis=0)
glm_est = run_GLM(raw_haemo, design_matrix)
df = glm_to_tidy(raw_haemo, glm_est, design_matrix)
df = _tidy_long_to_wide(df)
a = (df.query('condition in ["Control"]').groupby(['condition', 'Chroma'
]).agg(['mean']))
# Make sure false positive rate is less than 5%
assert a["Significant"].values[0] < 0.05
assert a["Significant"].values[1] < 0.05
a = (df.query('condition in ["Tapping/Left", "Tapping/Right"]').groupby(
['condition', 'Chroma']).agg(['mean']))
# Fairly arbitrary cutoff here, but its more than 5%
assert a["Significant"].values[0] > 0.7
assert a["Significant"].values[1] > 0.7
assert a["Significant"].values[2] > 0.7
assert a["Significant"].values[3] > 0.7
left = [[1, 1], [1, 2], [1, 3], [2, 1], [2, 3], [2, 4], [3, 2], [3, 3],
[4, 3], [4, 4]]
right = [[5, 5], [5, 6], [5, 7], [6, 5], [6, 7], [6, 8], [7, 6], [7, 7],
[8, 7], [8, 8]]
groups = dict(Left_ROI=picks_pair_to_idx(raw_haemo, left),
Right_ROI=picks_pair_to_idx(raw_haemo, right))
df = pd.DataFrame()
for idx, col in enumerate(design_matrix.columns[:3]):
df = df.append(glm_region_of_interest(glm_est, groups, idx, col))
assert df.shape == (12, 8)
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_io():
num_chans = 6
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
raw_intensity.resample(0.2)
raw_intensity.annotations.description[:] = [
'e' + d.replace('.', 'p')
for d in raw_intensity.annotations.description
]
raw_od = mne.preprocessing.nirs.optical_density(raw_intensity)
raw_haemo = mne.preprocessing.nirs.beer_lambert_law(raw_od, ppf=0.1)
raw_haemo = mne_nirs.channels.get_long_channels(raw_haemo)
raw_haemo.pick(picks=range(num_chans))
design_matrix = make_first_level_design_matrix(raw_intensity,
hrf_model='spm',
stim_dur=5.0,
drift_order=3,
drift_model='polynomial')
glm_est = run_glm(raw_haemo, design_matrix)
df = glm_to_tidy(raw_haemo, glm_est.data, design_matrix)
assert df.shape == (48, 12)
assert set(df.columns) == {
'ch_name', 'Condition', 'df', 'mse', 'p_value', 't', 'theta', 'Source',
'Detector', 'Chroma', 'Significant', 'se'
}
num_conds = 8 # triggers (1, 2, 3, 15) + 3 drifts + constant
assert df.shape[0] == num_chans * num_conds
assert len(df["se"]) == 48
assert sum(df["se"]) > 0 # Check isn't nan
assert len(df["df"]) == 48
assert sum(df["df"]) > 0 # Check isn't nan
assert len(df["p_value"]) == 48
assert sum(df["p_value"]) > 0 # Check isn't nan
assert len(df["theta"]) == 48
assert sum(df["theta"]) > 0 # Check isn't nan
assert len(df["t"]) == 48
assert sum(df["t"]) > -99999 # Check isn't nan
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['e2p0'] - basic_conts['e3p0']
contrast = mne_nirs.statistics.compute_contrast(glm_est.data, contrast_LvR)
df = glm_to_tidy(raw_haemo, contrast, design_matrix)
assert df.shape == (6, 10)
assert set(df.columns) == {
'ch_name', 'ContrastType', 'z_score', 'stat', 'p_value', 'effect',
'Source', 'Detector', 'Chroma', 'Significant'
}
contrast = mne_nirs.statistics.compute_contrast(glm_est.data,
contrast_LvR,
contrast_type='F')
df = glm_to_tidy(raw_haemo, contrast, design_matrix, wide=False)
df = _tidy_long_to_wide(df)
assert df.shape == (6, 10)
assert set(df.columns) == {
'ch_name', 'ContrastType', 'z_score', 'stat', 'p_value', 'effect',
'Source', 'Detector', 'Chroma', 'Significant'
}
with pytest.raises(TypeError, match="Unknown statistic type"):
glm_to_tidy(raw_haemo, [1, 2, 3], design_matrix, wide=False)
# likely to change. These functions are marked with an underscore (_)
# at the start of their name to indicate they are not public functions
# and have no promise they will be here next week.
#
# .. sidebar:: Relevant literature
#
# Wickham, Hadley. "Tidy data." Journal of Statistical Software 59.10 (2014): 1-23.
#
# Here we export the data in a tidy pandas data frame. Data is exported in
# long format by default.
# However, a helper function is also provided to convert the long data to wide format.
# The long to wide conversion also adds some additonal derived data, such as
# if a significant response (p<0.05) was observed, which sensor and detector is
# in the channel, which chroma, etc.
df = glm_to_tidy(raw_haemo, glm_est, design_matrix)
df = _tidy_long_to_wide(df)
###############################################################################
# Determine true and false positive rates
# ---------------------------------------
#
# We can query the exported data frames to determine the true and false
# positive rates. Note: optodes cover a greater region than just the
# motor cortex, so we dont expect 100% of channels to detect responses to
# the tapping, but we do expect 5% or less for the false positive rate.
(df.query('condition in ["Control", "Tapping/Left", "Tapping/Right"]').groupby(
['condition', 'Chroma']).agg(['mean']).drop(['df', 'mse', 'p_value', 't'],
1))