def test_flm_reporting():
with InTemporaryDirectory():
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 16)), 3
mask, fmri_data, design_matrices = _write_fake_fmri_data(shapes, rk)
flm = FirstLevelModel(mask_img=mask).fit(
fmri_data, design_matrices=design_matrices)
contrast = np.eye(3)[1]
report_flm = glmr.make_glm_report(
flm,
contrast,
plot_type='glass',
height_control=None,
min_distance=15,
alpha=0.001,
threshold=2.78,
)
'''
catches & raises UnicodeEncodeError in HTMLDocument.get_iframe()
Python2's limited unicode support causes HTMLDocument.get_iframe() to
mishandle certain unicode characters, like the greek alpha symbol
and raises this error.
Calling HTMLDocument.get_iframe() here causes the tests
to fail on Python2, alerting us if such a situation arises
due to future modifications.
'''
report_iframe = report_flm.get_iframe()
# So flake8 doesn't complain about not using variable (F841)
report_iframe
del mask, flm
def report_slm_oasis(): # pragma: no cover
n_subjects = 5 # more subjects requires more memory
oasis_dataset = nilearn.datasets.fetch_oasis_vbm(n_subjects=n_subjects)
# Resample the images, since this mask has a different resolution
mask_img = resample_to_img(
nilearn.datasets.fetch_icbm152_brain_gm_mask(),
oasis_dataset.gray_matter_maps[0],
interpolation='nearest',
)
design_matrix = _make_design_matrix_slm_oasis(oasis_dataset, n_subjects)
second_level_model = SecondLevelModel(smoothing_fwhm=2.0, mask=mask_img)
second_level_model.fit(oasis_dataset.gray_matter_maps,
design_matrix=design_matrix)
contrast = [[1, 0, 0], [0, 1, 0]]
report = make_glm_report(
model=second_level_model,
contrasts=contrast,
bg_img=nilearn.datasets.fetch_icbm152_2009()['t1'],
height_control=None,
)
output_filename = 'generated_report_slm_oasis.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def report_flm_fiac(): # pragma: no cover
data = nistats_datasets.fetch_fiac_first_level()
fmri_img = [data['func1'], data['func2']]
from nilearn.image import mean_img
mean_img_ = mean_img(fmri_img[0])
design_files = [data['design_matrix1'], data['design_matrix2']]
design_matrices = [pd.DataFrame(np.load(df)['X']) for df in design_files]
fmri_glm = FirstLevelModel(mask_img=data['mask'], minimize_memory=True)
fmri_glm = fmri_glm.fit(fmri_img, design_matrices=design_matrices)
n_columns = design_matrices[0].shape[1]
contrasts = {
'SStSSp_minus_DStDSp': _pad_vector([1, 0, 0, -1], n_columns),
'DStDSp_minus_SStSSp': _pad_vector([-1, 0, 0, 1], n_columns),
'DSt_minus_SSt': _pad_vector([-1, -1, 1, 1], n_columns),
'DSp_minus_SSp': _pad_vector([-1, 1, -1, 1], n_columns),
'DSt_minus_SSt_for_DSp': _pad_vector([0, -1, 0, 1], n_columns),
'DSp_minus_SSp_for_DSt': _pad_vector([0, 0, -1, 1], n_columns),
'Deactivation': _pad_vector([-1, -1, -1, -1, 4], n_columns),
'Effects_of_interest': np.eye(n_columns)[:5]
}
report = make_glm_report(
fmri_glm,
contrasts,
bg_img=mean_img_,
height_control='fdr',
)
output_filename = 'generated_report_flm_fiac.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def report_flm_bids_features(): # pragma: no cover
data_dir = _fetch_bids_data()
model, subject = _make_flm(data_dir)
title = 'FLM Bids Features Stat maps'
report = make_glm_report(
model=model,
contrasts='StopSuccess - Go',
title=title,
cluster_threshold=3,
)
output_filename = 'generated_report_flm_bids_features.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
def test_slm_reporting():
with InTemporaryDirectory():
shapes = ((7, 8, 9, 1), )
mask, FUNCFILE, _ = _write_fake_fmri_data(shapes)
FUNCFILE = FUNCFILE[0]
func_img = load(FUNCFILE)
model = SecondLevelModel()
Y = [func_img] * 4
X = pd.DataFrame([[1]] * 4, columns=['intercept'])
model = model.fit(Y, design_matrix=X)
c1 = np.eye(len(model.design_matrix_.columns))[0]
report_slm = glmr.make_glm_report(model, c1)
# catches & raises UnicodeEncodeError in HTMLDocument.get_iframe()
report_iframe = report_slm.get_iframe()
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del Y, FUNCFILE, func_img, model
def report_flm_adhd_dmn(): # pragma: no cover
t_r = 2.
slice_time_ref = 0.
n_scans = 176
pcc_coords = (0, -53, 26)
adhd_dataset = nilearn.datasets.fetch_adhd(n_subjects=1)
seed_masker = NiftiSpheresMasker([pcc_coords],
radius=10,
detrend=True,
standardize=True,
low_pass=0.1,
high_pass=0.01,
t_r=2.,
memory='nilearn_cache',
memory_level=1,
verbose=0)
seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
design_matrix = make_first_level_design_matrix(frametimes,
hrf_model='spm',
add_regs=seed_time_series,
add_reg_names=["pcc_seed"])
dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
contrasts = {'seed_based_glm': dmn_contrast}
first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0],
design_matrices=design_matrix)
report = make_glm_report(
first_level_model,
contrasts=contrasts,
title='ADHD DMN Report',
cluster_threshold=15,
height_control='bonferroni',
min_distance=8.,
plot_type='glass',
report_dims=(1200, 'a'),
)
output_filename = 'generated_report_flm_adhd_dmn.html'
output_filepath = os.path.join(REPORTS_DIR, output_filename)
report.save_as_html(output_filepath)
report.get_iframe()
