def test_high_level_non_parametric_inference_with_paths():
with InTemporaryDirectory():
n_perm = 100
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
c1 = np.eye(len(X.columns))[0]
neg_log_pvals_img = non_parametric_inference(Y,
design_matrix=X,
second_level_contrast=c1,
mask=mask,
n_perm=n_perm)
neg_log_pvals = get_data(neg_log_pvals_img)
assert isinstance(neg_log_pvals_img, Nifti1Image)
assert_array_equal(neg_log_pvals_img.affine, load(mask).affine)
assert np.all(neg_log_pvals <= -np.log10(1.0 / (n_perm + 1)))
assert np.all(0 <= neg_log_pvals)
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory
del X, Y, FUNCFILE, func_img, neg_log_pvals_img
def test_non_parametric_inference_permutation_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
neg_log_pvals_img = non_parametric_inference(Y, design_matrix=X,
mask=mask, n_perm=100)
assert get_data(neg_log_pvals_img).shape == shapes[0][:3]
del func_img, FUNCFILE, neg_log_pvals_img, X, Y
def test_non_parametric_inference_contrast_computation():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
# asking for contrast before model fit gives error
with pytest.raises(ValueError):
non_parametric_inference(None, None, None, 'intercept', mask)
# fit model
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
# formula should work without second-level contrast
neg_log_pvals_img = non_parametric_inference(Y, design_matrix=X,
mask=mask, n_perm=100)
ncol = len(X.columns)
c1, cnull = np.eye(ncol)[0, :], np.zeros(ncol)
# formula should work with second-level contrast
neg_log_pvals_img = non_parametric_inference(Y, design_matrix=X,
second_level_contrast=c1,
mask=mask, n_perm=100)
# formula should work passing variable name directly
neg_log_pvals_img = \
non_parametric_inference(Y, design_matrix=X,
second_level_contrast='intercept',
mask=mask, n_perm=100)
# passing null contrast should give back a value error
with pytest.raises(ValueError):
non_parametric_inference(Y, X, cnull, 'intercept', mask)
# passing wrong parameters
with pytest.raises(ValueError):
non_parametric_inference(Y, X, [], 'intercept', mask)
# check that passing no explicit contrast when the design
# matrix has more than one columns raises an error
X = pd.DataFrame(np.random.rand(4, 2), columns=['r1', 'r2'])
with pytest.raises(ValueError):
non_parametric_inference(Y, X, None)
del func_img, FUNCFILE, neg_log_pvals_img, X, Y
def test_fmri_inputs_for_non_parametric_inference():
# Test processing of FMRI inputs
with InTemporaryDirectory():
# prepare fake data
p, q = 80, 10
X = np.random.randn(p, q)
shapes = ((7, 8, 9, 10),)
mask, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
T = func_img.shape[-1]
des = pd.DataFrame(np.ones((T, 1)), columns=['a'])
des_fname = 'design.csv'
des.to_csv(des_fname)
# prepare correct input first level models
flm = FirstLevelModel(subject_label='01').fit(FUNCFILE,
design_matrices=des)
# prepare correct input dataframe and lists
shapes = ((7, 8, 9, 1),)
_, FUNCFILE, _ = write_fake_fmri_data_and_design(shapes)
FUNCFILE = FUNCFILE[0]
dfcols = ['subject_label', 'map_name', 'effects_map_path']
dfrows = [['01', 'a', FUNCFILE], ['02', 'a', FUNCFILE],
['03', 'a', FUNCFILE]]
niidf = pd.DataFrame(dfrows, columns=dfcols)
niimgs = [FUNCFILE, FUNCFILE, FUNCFILE]
niimg_4d = concat_imgs(niimgs)
confounds = pd.DataFrame([['01', 1], ['02', 2], ['03', 3]],
columns=['subject_label', 'conf1'])
sdes = pd.DataFrame(X[:3, :3], columns=['intercept', 'b', 'c'])
# test missing second-level contrast
# niimgs as input
with pytest.raises(ValueError):
non_parametric_inference(niimgs, None, sdes)
with pytest.raises(ValueError):
non_parametric_inference(niimgs, confounds, sdes)
# 4d niimg as input
with pytest.raises(ValueError):
non_parametric_inference(niimg_4d, None, sdes)
# test wrong input errors
# test first level model
with pytest.raises(ValueError):
non_parametric_inference(flm)
# test list of less than two niimgs
with pytest.raises(ValueError):
non_parametric_inference([FUNCFILE])
# test dataframe
with pytest.raises(ValueError):
non_parametric_inference(niidf)
# test niimgs requirements
with pytest.raises(ValueError):
non_parametric_inference(niimgs)
with pytest.raises(ValueError):
non_parametric_inference(niimgs + [[]], confounds)
with pytest.raises(ValueError):
non_parametric_inference([FUNCFILE])
# test other objects
with pytest.raises(ValueError):
non_parametric_inference('random string object')
del X, FUNCFILE, func_img
colorbar=True,
display_mode='z',
plot_abs=False,
vmax=3,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
###########################################################################
# Now, we compute the (corrected) p-values with a permutation test.
from nilearn.stats.second_level_model import non_parametric_inference
neg_log_pvals_permuted_ols_unmasked = \
non_parametric_inference(second_level_input,
design_matrix=design_matrix,
model_intercept=True, n_perm=1000,
two_sided_test=False,
smoothing_fwhm=8.0, n_jobs=1)
###########################################################################
# Let us plot the (corrected) negative log p-values for the nonparametric test.
title = ('Group left-right button press: \n' 'permutation test (FWER < 10%)')
display = plotting.plot_glass_brain(neg_log_pvals_permuted_ols_unmasked,
colorbar=True,
vmax=3,
display_mode='z',
plot_abs=False,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
title = ('Group-level association between motor activity and reading: \n'
'neg-log of parametric corrected p-values (FWER < 10%)')
plotting.plot_stat_map(neg_log_pval,
colorbar=True,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
##############################################################################
# Computing the (corrected) negative log p-values with permutation test
from nilearn.stats.second_level_model import non_parametric_inference
neg_log_pvals_permuted_ols_unmasked = \
non_parametric_inference(contrast_map_filenames,
design_matrix=design_matrix,
second_level_contrast='fluency',
model_intercept=True, n_perm=1000,
two_sided_test=False, mask=None,
smoothing_fwhm=5.0, n_jobs=1)
###########################################################################
# Let us plot the (corrected) negative log p-values
title = ('Group-level association between motor activity and reading: \n'
'neg-log of non-parametric corrected p-values (FWER < 10%)')
plotting.plot_stat_map(neg_log_pvals_permuted_ols_unmasked,
colorbar=True,
cut_coords=cut_coords,
threshold=threshold,
title=title)
plotting.show()
# The neg-log p-values obtained with non parametric testing are capped at 3