def test_invalid_thresholds_in_threshold_maps_ratio():
maps, _ = generate_maps((10, 11, 12), n_regions=2)
for invalid_threshold in ['80%', 'auto', -1.0]:
with pytest.raises(
ValueError,
match="threshold given as ratio to the number of voxels must "
"be Real number and should be positive "
"and between 0 and total number of maps "
"i.e. n_maps={0}. "
"You provided {1}".format(maps.shape[-1], invalid_threshold)):
_threshold_maps_ratio(maps, threshold=invalid_threshold)
def test_nans_threshold_maps_ratio():
maps, _ = generate_maps((10, 10, 10), n_regions=2)
data = get_data(maps)
data[:, :, 0] = np.nan
maps_img = nibabel.Nifti1Image(data, np.eye(4))
thr_maps = _threshold_maps_ratio(maps_img, threshold=0.8)
def test_threshold_maps_ratio():
# smoke test for function _threshold_maps_ratio with randomly
# generated maps
# make sure that n_regions (4th dimension) are kept same even
# in thresholded image
maps, _ = generate_maps((6, 8, 10), n_regions=3)
thr_maps = _threshold_maps_ratio(maps, threshold=1.0)
assert_true(thr_maps.shape[-1] == maps.shape[-1])
# check that the size should be same for 3D image
# before and after thresholding
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img = nibabel.Nifti1Image(img, affine=np.eye(4))
thr_maps_3d = _threshold_maps_ratio(img, threshold=0.5)
assert_true(img.shape == thr_maps_3d.shape)
def test_threshold_maps_ratio():
# smoke test for function _threshold_maps_ratio with randomly
# generated maps
# make sure that n_regions (4th dimension) are kept same even
# in thresholded image
maps, _ = generate_maps((6, 8, 10), n_regions=3)
thr_maps = _threshold_maps_ratio(maps, threshold=1.0)
assert_true(thr_maps.shape[-1] == maps.shape[-1])
# check that the size should be same for 3D image
# before and after thresholding
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img = nibabel.Nifti1Image(img, affine=np.eye(4))
thr_maps_3d = _threshold_maps_ratio(img, threshold=0.5)
assert_true(img.shape == thr_maps_3d.shape)
def test_nans_threshold_maps_ratio():
maps, _ = generate_maps((10, 10, 10), n_regions=2)
data = maps.get_data()
data[:, :, 0] = np.nan
maps_img = nibabel.Nifti1Image(data, np.eye(4))
thr_maps = _threshold_maps_ratio(maps_img, threshold=0.8)
def _region_extractor_cache(maps_img, mask_img=None, min_region_size=2500,
threshold=1., thresholding_strategy='ratio_n_voxels',
extractor='local_regions',
memory=Memory(cachedir=None), memory_level=0):
"""Region Extraction with caching built upon helper functions
See nilearn.regions.RegionExtractor for documentation and related
"""
if thresholding_strategy == 'ratio_n_voxels':
print("[Thresholding] using maps ratio with threshold={0}"
" ".format(threshold))
threshold_maps = _threshold_maps_ratio(maps_img, threshold)
else:
if thresholding_strategy == 'percentile':
threshold = "{0}%".format(threshold)
print("[Thresholding] using threshold_img with threshold={0}"
" ".format(threshold))
threshold_maps = threshold_img(maps_img, mask_img=mask_img,
threshold=threshold)
print("Done thresholding")
print("[Region Extraction] with {0}".format(extractor))
regions_img, _ = cache(connected_regions, memory,
memory_level=memory_level,
func_memory_level=1)(threshold_maps,
min_region_size=min_region_size,
extract_type=extractor)
return regions_img
def test_threshold_maps_ratio():
# smoke test for function _threshold_maps_ratio with randomly
# generated maps
maps, _ = generate_maps((6, 8, 10), n_regions=3)
# test that there is no side effect
get_data(maps)[:3] = 100
maps_data = get_data(maps).copy()
thr_maps = _threshold_maps_ratio(maps, threshold=1.0)
np.testing.assert_array_equal(get_data(maps), maps_data)
# make sure that n_regions (4th dimension) are kept same even
# in thresholded image
assert thr_maps.shape[-1] == maps.shape[-1]
# check that the size should be same for 3D image
# before and after thresholding
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img = nibabel.Nifti1Image(img, affine=np.eye(4))
thr_maps_3d = _threshold_maps_ratio(img, threshold=0.5)
assert img.shape == thr_maps_3d.shape
def test_threshold_maps_ratio():
# smoke test for function _threshold_maps_ratio with randomly
# generated maps
maps, _ = generate_maps((6, 8, 10), n_regions=3)
# test that there is no side effect
maps.get_data()[:3] = 100
maps_data = maps.get_data().copy()
thr_maps = _threshold_maps_ratio(maps, threshold=1.0)
np.testing.assert_array_equal(maps.get_data(), maps_data)
# make sure that n_regions (4th dimension) are kept same even
# in thresholded image
assert_true(thr_maps.shape[-1] == maps.shape[-1])
# check that the size should be same for 3D image
# before and after thresholding
img = np.zeros((30, 30, 30)) + 0.1 * np.random.randn(30, 30, 30)
img = nibabel.Nifti1Image(img, affine=np.eye(4))
thr_maps_3d = _threshold_maps_ratio(img, threshold=0.5)
assert_true(img.shape == thr_maps_3d.shape)
