# Instantiate the mask and visualize atlas # from nilearn.input_data import NiftiLabelsMasker # Instantiate the masker with label image and label values masker = NiftiLabelsMasker(atlas.maps, labels=atlas.labels, standardize=True) # Visualize the atlas # Note that we need to call fit prior to generating the mask masker.fit() # At this point, no functional image has been provided to the masker. # We can still generate a report which can be displayed in a Jupyter # Notebook, opened in a browser using the .open_in_browser() method, # or saved to a file using the .save_as_html(output_filepath) mathod. report = masker.generate_report() report ########################################################################## # Fitting the mask and generating a report masker.fit(func_filename) # We can again generate a report, but this time, the provided functional # image is displayed with the ROI of the atlas. # The report also contains a summary table giving the region sizes in mm3 report = masker.generate_report() report ########################################################################### # Process the data with the NiftiLablesMasker #
def test_nifti_labels_masker_report(data_img_3d, mask):
shape = (13, 11, 12)
affine = np.diag([2, 2, 2, 1])
n_regions = 9
labels = ['background'
] + ['region_{}'.format(i) for i in range(1, n_regions + 1)]
EXPECTED_COLUMNS = [
'label value', 'region name', 'size (in mm^3)', 'relative size (in %)'
]
labels_img = generate_labeled_regions(shape,
affine=affine,
n_regions=n_regions)
labels_img_floats = new_img_like(labels_img,
get_data(labels_img).astype(float))
masker = NiftiLabelsMasker(labels_img_floats, labels=labels)
masker.fit()
masker.generate_report()
# Check that providing incorrect labels raises an error
masker = NiftiLabelsMasker(labels_img, labels=labels[:-1])
masker.fit()
with pytest.raises(ValueError,
match="Mismatch between the number of provided labels"):
masker.generate_report()
masker = NiftiLabelsMasker(labels_img, labels=labels)
masker.fit()
# Check that a warning is given when generating the report
# since no image was provided to fit
with pytest.warns(UserWarning,
match="No image provided to fit in NiftiLabelsMasker"):
masker.generate_report()
# No image was provided to fit, regions are plotted using
# plot_roi such that no contour should be in the image
display = masker._reporting()
for d in ['x', 'y', 'z']:
assert len(display[0].axes[d].ax.collections) == 0
masker = NiftiLabelsMasker(labels_img, labels=labels)
masker.fit(data_img_3d)
display = masker._reporting()
for d in ['x', 'y', 'z']:
assert len(display[0].axes[d].ax.collections) > 0
assert len(display[0].axes[d].ax.collections) <= n_regions
masker = NiftiLabelsMasker(labels_img, labels=labels, mask_img=mask)
masker.fit(data_img_3d)
report = masker.generate_report()
assert masker._reporting_data is not None
# Check that background label was left as default
assert masker.background_label == 0
assert masker._report_content['description'] == (
'This reports shows the regions defined by the labels of the mask.')
# Check that the number of regions is correct
assert masker._report_content['number_of_regions'] == n_regions
# Check that all expected columns are present with the right size
for col in EXPECTED_COLUMNS:
assert col in masker._report_content['summary']
assert len(masker._report_content['summary'][col]) == n_regions
# Check that labels match
assert masker._report_content['summary']['region name'] == labels[1:]
# Relative sizes of regions should sum to 100%
assert_almost_equal(
sum(masker._report_content['summary']['relative size (in %)']), 100)
_check_html(report)
assert "Regions summary" in str(report)
# Check region sizes calculations
expected_region_sizes = Counter(get_data(labels_img).ravel())
for r in range(1, n_regions + 1):
assert_almost_equal(
masker._report_content['summary']['size (in mm^3)'][r - 1],
expected_region_sizes[r] * np.abs(np.linalg.det(affine[:3, :3])))
# Check that region labels are no displayed in the report
# when they were not provided by the user.
masker = NiftiLabelsMasker(labels_img)
masker.fit()
report = masker.generate_report()
for col in EXPECTED_COLUMNS:
if col == "region name":
assert col not in masker._report_content["summary"]
else:
assert col in masker._report_content["summary"]
assert len(masker._report_content['summary'][col]) == n_regions
