def test_sample_mask():
"""Test load method and sample mask."""
# create a version with srub_mask not applied;
# This is not recommanded
conf = lc.Confounds(strategy=["motion", "scrub"],
scrub="full",
fd_thresh=0.15)
reg, mask = conf.load(file_confounds)
# the current test data has 6 time points marked as motion outliers,
# and one nonsteady state (overlap with the first motion outlier)
# 2 time points removed due to the "full" srubbing strategy
assert reg.shape[0] - len(mask) == 8
# nilearn requires unmasked confound regressors
assert reg.shape[0] == 30
# non steady state will always be removed
conf = lc.Confounds(strategy=["motion"])
reg, mask = conf.load(file_confounds)
assert reg.shape[0] - len(mask) == 1
# When no non-steady state volumes are present
conf = lc.Confounds(strategy=["motion"])
reg, mask = conf.load(file_no_none_steady)
assert mask is None
def test_not_found_exception():
conf = lc.Confounds(strategy=["high_pass", "motion", "global"],
global_signal="full",
motion="full")
missing_params = ["trans_y", "trans_x_derivative1", "rot_z_power2"]
missing_keywords = ["cosine"]
file_missing_confounds = os.path.join(
path_data,
"missing_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz")
with pytest.raises(ValueError) as exc_info:
conf.load(file_missing_confounds)
assert f"{missing_params}" in exc_info.value.args[0]
assert f"{missing_keywords}" in exc_info.value.args[0]
# loading anat compcor should also raise an error, because the json file is
# missing for that example dataset
with pytest.raises(ValueError):
conf = lc.Confounds(strategy=["compcor"], compcor="anat")
conf.load(file_missing_confounds)
# catch invalid compcor option
with pytest.raises(KeyError):
conf = lc.Confounds(strategy=["compcor"], compcor="blah")
conf.load(file_confounds)
# catch invalid compcor option
with pytest.raises(ValueError):
conf = lc.Confounds(strategy=["compcor"],
compcor="full",
acompcor_combined=None)
conf.load(file_confounds)
def test_sanitize_strategy():
"""Check that flawed strategy options generate meaningful error messages."""
with pytest.raises(ValueError):
lc.Confounds(strategy="string")
with pytest.raises(ValueError):
lc.Confounds(strategy=["error"])
with pytest.raises(ValueError):
lc.Confounds(strategy=[0])
def test_sanitize_strategy():
"""Check that flawed strategy options generate meaningful error messages."""
with pytest.raises(ValueError):
lc.Confounds(strategy="string")
with pytest.raises(ValueError):
lc.Confounds(strategy=["error"])
with pytest.raises(ValueError):
lc.Confounds(strategy=[0])
conf = lc.Confounds(strategy=["motion"])
assert "non_steady_state" in conf.strategy
def test_n_motion():
conf = lc.Confounds(strategy=["motion"], motion="full", n_motion=0.2)
conf.load(file_confounds)
assert "motion_pca_1" in conf.confounds_.columns
assert "motion_pca_2" not in conf.confounds_.columns
conf = lc.Confounds(strategy=["motion"], motion="full", n_motion=0.95)
conf.load(file_confounds)
assert "motion_pca_6" in conf.confounds_.columns
with pytest.raises(ValueError):
conf = lc.Confounds(strategy=["motion"], motion="full", n_motion=50)
conf.load(file_confounds)
def test_n_compcor():
conf = lc.Confounds(strategy=["compcor"], compcor="anat", n_compcor=2)
conf.load(file_confounds)
assert "a_comp_cor_00" in conf.confounds_.columns
assert "a_comp_cor_01" in conf.confounds_.columns
assert "a_comp_cor_02" not in conf.confounds_.columns
def test_confounds2df():
"""Check auto-detect of confonds from an fMRI nii image."""
conf = lc.Confounds()
file_confounds_nii = os.path.join(
path_data, "test_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz")
conf.load(file_confounds_nii)
assert "trans_x" in conf.confounds_.columns
def test_nilearn_regress():
"""Try regressing out all motion types in nilearn."""
# Regress full motion
confounds, _ = lc.Confounds(strategy=["motion"],
motion="full").load(file_confounds)
sample_mask = None # not testing sample mask here
_regression(confounds, sample_mask)
# Regress high_pass
confounds, _ = lc.Confounds(strategy=["high_pass"]).load(file_confounds)
_regression(confounds, sample_mask)
# Regress wm_csf
confounds, _ = lc.Confounds(strategy=["wm_csf"],
wm_csf="full").load(file_confounds)
_regression(confounds, sample_mask)
# Regress global
confounds, _ = lc.Confounds(strategy=["global"],
global_signal="full").load(file_confounds)
_regression(confounds, sample_mask)
# Regress AnatCompCor
confounds, _ = lc.Confounds(strategy=["compcor"],
compcor="anat").load(file_confounds)
_regression(confounds, sample_mask)
# Regress TempCompCor
confounds, _ = lc.Confounds(strategy=["compcor"],
compcor="temp").load(file_confounds)
_regression(confounds, sample_mask)
# Regress ICA-AROMA
confounds, _ = lc.Confounds(strategy=["ica_aroma"],
ica_aroma="basic").load(file_confounds)
_regression(confounds, sample_mask)
def test_read_file():
"""Check that loading missing or incomplete files produce error messages."""
conf = lc.Confounds()
with pytest.raises(FileNotFoundError):
conf.load(" ")
with pytest.raises(ValueError):
df = pd.read_csv(file_confounds, sep="\t")
df = df.drop("trans_x", axis=1)
conf.load(df)
def test_nilearn_regress():
"""Try regressing out all motion types in nilearn."""
# Regress full motion
confounds = lc.Confounds(strategy=["motion"],
motion="full").load(file_confounds)
_regression(confounds)
# Regress high_pass
confounds = lc.Confounds(strategy=["high_pass"]).load(file_confounds)
_regression(confounds)
# Regress wm_csf
confounds = lc.Confounds(strategy=["wm_csf"],
wm_csf="full").load(file_confounds)
_regression(confounds)
# Regress global
confounds = lc.Confounds(strategy=["global"],
global_signal="full").load(file_confounds)
_regression(confounds)
# Regress AnatCompCor
confounds = lc.Confounds(strategy=["compcor"],
compcor="anat").load(file_confounds)
_regression(confounds)
# Regress TempCompCor
confounds = lc.Confounds(strategy=["compcor"],
compcor="temp").load(file_confounds)
_regression(confounds)
def test_ica_aroma():
"""Test ICA AROMA related file input."""
aroma_nii = os.path.join(
path_data,
"test_space-MNI152NLin2009cAsym_desc-smoothAROMAnonaggr_bold.nii.gz")
# Agressive strategy
conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="basic")
conf.load(file_confounds)
for col_name in conf.confounds_.columns:
# only aroma and non-steady state columns will be present
assert re.match("(?:aroma_motion_+|non_steady_state+)", col_name)
# Non-agressive strategy
conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="full")
conf.load(aroma_nii)
assert conf.confounds_.size == 0
# invalid combination of strategy and option
with pytest.raises(ValueError) as exc_info:
conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma=None)
conf.load(file_confounds)
assert "ICA-AROMA strategy" in exc_info.value.args[0]
def test_not_found_exception():
conf = lc.Confounds(strategy=["high_pass", "motion", "global"],
global_signal="full",
motion="full")
missing_params = ["trans_y", "trans_x_derivative1", "rot_z_power2"]
missing_keywords = ["cosine"]
file_missing_confounds = os.path.join(
path_data,
"missing_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz")
with pytest.raises(ValueError) as exc_info:
conf.load(file_missing_confounds)
assert f"{missing_params}" in exc_info.value.args[0]
assert f"{missing_keywords}" in exc_info.value.args[0]
# loading anat compcor should also raise an error, because the json file is
# missing for that example dataset
with pytest.raises(ValueError):
conf = lc.Confounds(strategy=["compcor"], compcor="anat")
conf.load(file_missing_confounds)
# catch invalid compcor option
with pytest.raises(KeyError):
conf = lc.Confounds(strategy=["compcor"], compcor="blah")
conf.load(file_confounds)
# catch invalid compcor option
with pytest.raises(ValueError):
conf = lc.Confounds(strategy=["compcor"],
compcor="full",
acompcor_combined=None)
conf.load(file_confounds)
# Aggressive ICA-AROMA strategy requires
# default nifti and noise ICs in confound file
# correct nifti but missing noise regressor
with pytest.raises(ValueError) as exc_info:
conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="basic")
conf.load(file_missing_confounds)
assert "aroma" in exc_info.value.args[0]
# Aggressive ICA-AROMA strategy requires
# default nifti
aroma_nii = os.path.join(
path_data,
"test_space-MNI152NLin2009cAsym_desc-smoothAROMAnonaggr_bold.nii.gz")
with pytest.raises(ValueError) as exc_info:
conf.load(aroma_nii)
assert "Invalid file type" in exc_info.value.args[0]
# non aggressive ICA-AROMA strategy requires
# desc-smoothAROMAnonaggr nifti file
with pytest.raises(ValueError) as exc_info:
conf = lc.Confounds(strategy=["ica_aroma"], ica_aroma="full")
conf.load(file_missing_confounds)
assert "desc-smoothAROMAnonaggr_bold" in exc_info.value.args[0]
def _simu_img(demean=True):
"""Simulate an nifti image based on confound file with some parts confounds and some parts noise."""
# set the size of the image matrix
nx = 5
ny = 5
# the actual number of slices will actually be double of that
# as we will stack slices with confounds on top of slices with noise
nz = 2
# Load a simple 6 parameters motion models as confounds
confounds, _ = lc.Confounds(strategy=["motion"],
motion="basic",
demean=demean).load(file_confounds)
X = confounds.values
# the first row is non-steady state, replace it with the imput from the second row
non_steady = X[0, :]
X[0, :] = X[1, :]
# repeat X in length (axis = 0) three times to increase the degree of freedom
X = np.tile(X, (3, 1))
# put non-steady state volume back at the first sample
X[0, :] = non_steady
# the number of time points is based on the example confound file
nt = X.shape[0]
# initialize an empty 4D volume
vol = np.zeros([nx, ny, 2 * nz, nt])
vol_conf = np.zeros([nx, ny, 2 * nz])
vol_rand = np.zeros([nx, ny, 2 * nz])
# create a random mixture of confounds
# standardized to zero mean and unit variance
beta = np.random.rand(nx * ny * nz, X.shape[1])
tseries_conf = scale(np.matmul(beta, X.transpose()), axis=1)
# fill the first half of the 4D data with the mixture
vol[:, :, 0:nz, :] = tseries_conf.reshape(nx, ny, nz, nt)
vol_conf[:, :, 0:nz] = 1
# create random noise in the second half of the 4D data
tseries_rand = scale(np.random.randn(nx * ny * nz, nt), axis=1)
vol[:, :, range(nz, 2 * nz), :] = tseries_rand.reshape(nx, ny, nz, nt)
vol_rand[:, :, range(nz, 2 * nz)] = 1
# Shift the mean to non-zero
vol = vol + 100
# create an nifti image with the data, and corresponding mask
img = Nifti1Image(vol, np.eye(4))
mask_conf = Nifti1Image(vol_conf, np.eye(4))
mask_rand = Nifti1Image(vol_rand, np.eye(4))
return img, mask_conf, mask_rand, X
def test_motion():
conf_basic = lc.Confounds(strategy=["motion"], motion="basic")
conf_basic.load(file_confounds)
conf_derivatives = lc.Confounds(strategy=["motion"], motion="derivatives")
conf_derivatives.load(file_confounds)
conf_power2 = lc.Confounds(strategy=["motion"], motion="power2")
conf_power2.load(file_confounds)
conf_full = lc.Confounds(strategy=["motion"], motion="full")
conf_full.load(file_confounds)
params = ["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"]
for param in params:
# Basic 6 params motion model
assert f"{param}" in conf_basic.confounds_.columns
assert f"{param}_derivative1" not in conf_basic.confounds_.columns
assert f"{param}_power2" not in conf_basic.confounds_.columns
assert f"{param}_derivative1_power2" not in conf_basic.confounds_.columns
# Use a 6 params + derivatives motion model
assert f"{param}" in conf_derivatives.confounds_.columns
assert f"{param}_derivative1" in conf_derivatives.confounds_.columns
assert f"{param}_power2" not in conf_derivatives.confounds_.columns
assert f"{param}_derivative1_power2" not in conf_derivatives.confounds_.columns
# Use a 6 params + power2 motion model
assert f"{param}" in conf_power2.confounds_.columns
assert f"{param}_derivative1" not in conf_power2.confounds_.columns
assert f"{param}_power2" in conf_power2.confounds_.columns
assert f"{param}_derivative1_power2" not in conf_power2.confounds_.columns
# Use a 6 params + derivatives + power2 + power2d derivatives motion model
assert f"{param}" in conf_full.confounds_.columns
assert f"{param}_derivative1" in conf_full.confounds_.columns
assert f"{param}_power2" in conf_full.confounds_.columns
assert f"{param}_derivative1_power2" in conf_full.confounds_.columns
def test_invalid_filetype():
"""Invalid file types/associated files for load method."""
# invalid fmriprep version: contain confound files before and after v20.2.0
conf = lc.Confounds()
invalid_ver = os.path.join(
path_data,
"invalid_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz")
with pytest.raises(ValueError):
conf.load(invalid_ver)
# nifti with no associated confound file
no_confound = os.path.join(
path_data,
"noconfound_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz")
with pytest.raises(ValueError):
conf.load(no_confound)
def test_load_non_nifti():
"""Test non-nifti and invalid file type as input."""
conf = lc.Confounds()
# tsv file - unsupported input
tsv = os.path.join(path_data, "test_desc-confounds_regressors.tsv")
with pytest.raises(ValueError):
conf.load(tsv)
# cifti file should be supported
cifti = os.path.join(path_data,
"test_space-fsLR_den-91k_bold.dtseries.nii")
conf.load(cifti)
assert conf.confounds_.size != 0
# gifti support
gifti = [
os.path.join(path_data,
f"test_space-fsaverage5_hemi-{hemi}_bold.func.gii")
for hemi in ["L", "R"]
]
conf.load(gifti)
assert conf.confounds_.size != 0
def test_ica_aroma():
conf = lc.Confounds(strategy=["ica_aroma"])
conf.load(file_confounds)
for col_name in conf.columns_:
assert re.match("aroma_motion_+", col_name)
