def test_first_level_model_design_creation():
# Test processing of FMRI inputs
with InTemporaryDirectory():
shapes = ((7, 8, 9, 10), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# basic test based on basic_paradigm and glover hrf
t_r = 10.0
slice_time_ref = 0.
events = basic_paradigm()
model = FirstLevelModel(t_r,
slice_time_ref,
mask_img=mask,
drift_model='polynomial',
drift_order=3)
model = model.fit(func_img, events)
frame1, X1, names1 = check_design_matrix(model.design_matrices_[0])
# check design computation is identical
n_scans = get_data(func_img).shape[3]
start_time = slice_time_ref * t_r
end_time = (n_scans - 1 + slice_time_ref) * t_r
frame_times = np.linspace(start_time, end_time, n_scans)
design = make_first_level_design_matrix(frame_times,
events,
drift_model='polynomial',
drift_order=3)
frame2, X2, names2 = check_design_matrix(design)
assert_array_equal(frame1, frame2)
assert_array_equal(X1, X2)
assert_array_equal(names1, names2)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del FUNCFILE, mask, model, func_img
def test_design_matrix0():
# Test design matrix creation when no experimental paradigm is provided
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
_, X, names = check_design_matrix(
make_first_level_design_matrix(frame_times,
drift_model='polynomial',
drift_order=3))
assert len(names) == 4
x = np.linspace(-0.5, .5, 128)
assert_almost_equal(X[:, 0], x)
def test_csv_io():
# test the csv io on design matrices
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
events = modulated_event_paradigm()
DM = make_first_level_design_matrix(
frame_times,
events,
hrf_model='glover',
drift_model='polynomial',
drift_order=3,
)
path = 'design_matrix.csv'
with InTemporaryDirectory():
DM.to_csv(path)
DM2 = pd.read_csv(path, index_col=0)
_, matrix, names = check_design_matrix(DM)
_, matrix_, names_ = check_design_matrix(DM2)
assert_almost_equal(matrix, matrix_)
assert names == names_
def test_design_matrix0d():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(
make_first_level_design_matrix(frame_times,
drift_model='polynomial',
drift_order=3,
add_regs=ax))
assert len(names) == 8
assert X.shape[1] == 8
def test_spm_2():
# Check that the nistats design matrix is close enough to the SPM one
# (it cannot be identical, because the hrf shape is different)
frame_times = np.linspace(0, 99, 100)
conditions = ['c0', 'c0', 'c0', 'c1', 'c1', 'c1', 'c2', 'c2', 'c2']
onsets = [30, 50, 70, 10, 30, 80, 30, 40, 60]
durations = 10 * np.ones(9)
events = pd.DataFrame({
'trial_type': conditions,
'onset': onsets,
'duration': durations
})
X1 = make_first_level_design_matrix(frame_times, events, drift_model=None)
spm_design_matrix = DESIGN_MATRIX['arr_1']
_, matrix, _ = check_design_matrix(X1)
assert (((spm_design_matrix - matrix)**2).sum() /
(spm_design_matrix**2).sum() < .1)
def design_matrix_light(frame_times,
events=None,
hrf_model='glover',
drift_model='cosine',
high_pass=.01,
drift_order=1,
fir_delays=None,
add_regs=None,
add_reg_names=None,
min_onset=-24,
path=None):
""" Same as make_first_level_design_matrix,
but only returns the computed matrix and associated name.
"""
fir_delays = fir_delays if fir_delays else [0]
dmtx = make_first_level_design_matrix(frame_times, events, hrf_model,
drift_model, high_pass, drift_order,
fir_delays, add_regs, add_reg_names,
min_onset)
_, matrix, names = check_design_matrix(dmtx)
return matrix, names
def test_design_matrix0c():
# test design matrix creation when regressors are provided manually
tr = 1.0
frame_times = np.linspace(0, 127 * tr, 128)
ax = np.random.randn(128, 4)
_, X, names = check_design_matrix(
make_first_level_design_matrix(frame_times,
drift_model='polynomial',
drift_order=3,
add_regs=ax))
assert_almost_equal(X[:, 0], ax[:, 0])
ax = np.random.randn(127, 4)
with pytest.raises(
AssertionError,
match="Incorrect specification of additional regressors:."):
make_first_level_design_matrix(frame_times, add_regs=ax)
ax = np.random.randn(128, 4)
with pytest.raises(
ValueError,
match="Incorrect number of additional regressor names."):
make_first_level_design_matrix(frame_times,
add_regs=ax,
add_reg_names='')