def test_compute_epi_mask():
mean_image = np.ones((9, 9, 3))
mean_image[3:-2, 3:-2, :] = 10
mean_image[5, 5, :] = 11
mean_image = Nifti1Image(mean_image, np.eye(4))
mask1 = compute_epi_mask(mean_image, opening=False)
mask2 = compute_epi_mask(mean_image, exclude_zeros=True, opening=False)
# With an array with no zeros, exclude_zeros should not make
# any difference
np.testing.assert_array_equal(get_data(mask1), get_data(mask2))
# Check that padding with zeros does not change the extracted mask
mean_image2 = np.zeros((30, 30, 3))
mean_image2[3:12, 3:12, :] = get_data(mean_image)
mean_image2 = Nifti1Image(mean_image2, np.eye(4))
mask3 = compute_epi_mask(mean_image2, exclude_zeros=True, opening=False)
np.testing.assert_array_equal(get_data(mask1), get_data(mask3)[3:12, 3:12])
# However, without exclude_zeros, it does
mask3 = compute_epi_mask(mean_image2, opening=False)
assert not np.allclose(get_data(mask1), get_data(mask3)[3:12, 3:12])
# Check that we get a ValueError for incorrect shape
mean_image = np.ones((9, 9))
mean_image[3:-3, 3:-3] = 10
mean_image[5, 5] = 100
mean_image = Nifti1Image(mean_image, np.eye(4))
pytest.raises(ValueError, compute_epi_mask, mean_image)
# Check that we get a useful warning for empty masks
mean_image = np.zeros((9, 9, 9))
mean_image[0, 0, 1] = -1
mean_image[0, 0, 0] = 1.2
mean_image[0, 0, 2] = 1.1
mean_image = Nifti1Image(mean_image, np.eye(4))
with pytest.warns(MaskWarning, match='Computed an empty mask'):
compute_epi_mask(mean_image, exclude_zeros=True)
def test_dict_learning():
data, mask_img, components, rng = _make_canica_test_data(n_subjects=8)
masker = NiftiMasker(mask_img=mask_img).fit()
mask = get_data(mask_img) != 0
flat_mask = mask.ravel()
dict_init = masker.inverse_transform(components[:, flat_mask])
dict_learning = DictLearning(n_components=4, random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0., alpha=1)
dict_learning_auto_init = DictLearning(n_components=4, random_state=0,
mask=mask_img,
smoothing_fwhm=0., n_epochs=10,
alpha=1)
maps = {}
for estimator in [dict_learning,
dict_learning_auto_init]:
estimator.fit(data)
maps[estimator] = get_data(estimator.components_img_)
maps[estimator] = np.reshape(
np.rollaxis(maps[estimator], 3, 0)[:, mask],
(4, flat_mask.sum()))
masked_components = components[:, flat_mask]
for this_dict_learning in [dict_learning]:
these_maps = maps[this_dict_learning]
S = np.sqrt(np.sum(masked_components ** 2, axis=1))
S[S == 0] = 1
masked_components /= S[:, np.newaxis]
S = np.sqrt(np.sum(these_maps ** 2, axis=1))
S[S == 0] = 1
these_maps /= S[:, np.newaxis]
K = np.abs(masked_components.dot(these_maps.T))
recovered_maps = np.sum(K > 0.9)
assert(recovered_maps >= 2)
# Smoke test n_epochs > 1
dict_learning = DictLearning(n_components=4, random_state=0,
dict_init=dict_init,
mask=mask_img,
smoothing_fwhm=0., n_epochs=2, alpha=1)
dict_learning.fit(data)
def significance_map(subject_scores, mask):
"""
Parameters
----------
subject_scores : list
list of niimgs or paths to NIfTI files representing the MVPA scores for
each subject
mask : Niimg-like object
boolean image giving location of voxels containing usable signals
Returns
-------
t_map : numpy.ndarray
array of t-statistic values indicating the significance of each voxel
for condition A; same shape as the mask
p_map : numpy.ndarray
array of p-values associated with the t-statistics in t_map
"""
score_maps = []
for score_map in subject_scores:
if isinstance(score_map, str):
score_map = get_data(load_img(score_map))
else:
score_map = get_data(score_map)
score_maps.append(np.expand_dims(score_map, axis=3))
mask = np.mean(score_maps, axis=0).astype(bool)
score_maps = np.concatenate(score_maps, axis=-1)
t_map = np.zeros_like(mask, dtype=np.float64)
p_map = np.zeros_like(mask, dtype=np.float64)
for x in range(mask.shape[0]):
for y in range(mask.shape[1]):
for z in range(mask.shape[2]):
if not mask[x][y][z]:
continue
# Significance values from each subject for one voxel
sample = score_maps[x][y][z]
test = stats.ttest_1samp(sample, popmean=0.0)
t_map[x][y][z] = test.statistic
p_map[x][y][z] = test.pvalue
return t_map, p_map
def test_pd_index_img():
# confirm indices from pandas dataframes are handled correctly
if 'pandas' not in sys.modules:
raise pytest.skip(msg='Pandas not available')
affine = np.array([[1., 2., 3., 4.], [5., 6., 7., 8.], [9., 10., 11., 12.],
[0., 0., 0., 1.]])
img_4d, _ = data_gen.generate_fake_fmri(affine=affine)
fourth_dim_size = img_4d.shape[3]
rng = np.random.RandomState(42)
arr = rng.uniform(size=fourth_dim_size) > 0.5
df = pd.DataFrame({"arr": arr})
np_index_img = image.index_img(img_4d, arr)
pd_index_img = image.index_img(img_4d, df)
assert_array_equal(get_data(np_index_img), get_data(pd_index_img))
def test_compute_multi_gray_matter_mask():
pytest.raises(TypeError, compute_multi_gray_matter_mask, [])
# Check error raised if images with different shapes are given as input
imgs = [Nifti1Image(np.ones((9, 9, 9)), np.eye(4)),
Nifti1Image(np.ones((9, 9, 8)), np.eye(4))]
pytest.raises(ValueError, compute_multi_gray_matter_mask, imgs)
# Check results are the same if affine is the same
imgs1 = [Nifti1Image(np.random.randn(9, 9, 9), np.eye(4)),
Nifti1Image(np.random.randn(9, 9, 9), np.eye(4))]
mask1 = compute_multi_gray_matter_mask(imgs1)
imgs2 = [Nifti1Image(np.random.randn(9, 9, 9), np.eye(4)),
Nifti1Image(np.random.randn(9, 9, 9), np.eye(4))]
mask2 = compute_multi_gray_matter_mask(imgs2)
assert_array_equal(get_data(mask1), get_data(mask2))
def get_block(image, block_height):
N = image.get_data().astype(float)
N -= N.min()
if N.max() > 0: N /= N.max()
N = (N * 256).astype('uint16')
# ^ normalize into 0 - 256 range
mid = len(N) // 2
half = block_height // 2
return N[mid - half:mid + half]
def test_mean_img_resample():
# Test resampling in mean_img with a permutation of the axes
rng = np.random.RandomState(42)
data = rng.rand(5, 6, 7, 40)
affine = np.diag((4, 3, 2, 1))
img = nibabel.Nifti1Image(data, affine=affine)
mean_img = nibabel.Nifti1Image(data.mean(axis=-1), affine=affine)
target_affine = affine[:, [1, 0, 2, 3]] # permutation of axes
mean_img_with_resampling = image.mean_img(img,
target_affine=target_affine)
resampled_mean_image = resampling.resample_img(mean_img,
target_affine=target_affine)
assert_array_equal(get_data(resampled_mean_image),
get_data(mean_img_with_resampling))
assert_array_equal(resampled_mean_image.affine,
mean_img_with_resampling.affine)
assert_array_equal(mean_img_with_resampling.affine, target_affine)
def test_isnan_threshold_img_data():
shape = (10, 10, 10)
maps, _ = data_gen.generate_maps(shape, n_regions=2)
data = get_data(maps)
data[:, :, 0] = np.nan
maps_img = nibabel.Nifti1Image(data, np.eye(4))
# test threshold_img to converge properly when input image has nans.
threshold_img(maps_img, threshold=0.8)
def test_crop():
# Testing that padding of arrays and cropping of images work symmetrically
shape = (4, 6, 2)
data = np.ones(shape)
padded = _pad_array(data, [3, 2, 4, 4, 5, 7])
padd_nii = Nifti1Image(padded, np.eye(4))
cropped = crop_img(padd_nii, pad=False)
np.testing.assert_equal(get_data(cropped), data)
def test_smooth_img():
# This function only checks added functionalities compared
# to _smooth_array()
shapes = ((10, 11, 12), (13, 14, 15))
lengths = (17, 18)
fwhm = (1., 2., 3.)
img1, mask1 = data_gen.generate_fake_fmri(shape=shapes[0],
length=lengths[0])
img2, mask2 = data_gen.generate_fake_fmri(shape=shapes[1],
length=lengths[1])
for create_files in (False, True):
with testing.write_tmp_imgs(img1, img2,
create_files=create_files) as imgs:
# List of images as input
out = image.smooth_img(imgs, fwhm)
assert isinstance(out, list)
assert len(out) == 2
for o, s, l in zip(out, shapes, lengths):
assert o.shape == (s + (l, ))
# Single image as input
out = image.smooth_img(imgs[0], fwhm)
assert isinstance(out, nibabel.Nifti1Image)
assert out.shape == (shapes[0] + (lengths[0], ))
# Check corner case situations when fwhm=0, See issue #1537
# Test whether function smooth_img raises a warning when fwhm=0.
with pytest.warns(UserWarning):
image.smooth_img(img1, fwhm=0.)
# Test output equal when fwhm=None and fwhm=0
out_fwhm_none = image.smooth_img(img1, fwhm=None)
out_fwhm_zero = image.smooth_img(img1, fwhm=0.)
assert_array_equal(get_data(out_fwhm_none), get_data(out_fwhm_zero))
data1 = np.zeros((10, 11, 12))
data1[2:4, 1:5, 3:6] = 1
data2 = np.zeros((13, 14, 15))
data2[2:4, 1:5, 3:6] = 9
img1_nifti2 = nibabel.Nifti2Image(data1, affine=np.eye(4))
img2_nifti2 = nibabel.Nifti2Image(data2, affine=np.eye(4))
out = image.smooth_img([img1_nifti2, img2_nifti2], fwhm=1.)
def test_resampling_error_checks():
rng = np.random.RandomState(42)
shape = (3, 2, 5, 2)
target_shape = (5, 3, 2)
affine = np.eye(4)
data = rng.randint(0, 10, shape)
img = Nifti1Image(data, affine)
# Correct parameters: no exception
resample_img(img, target_shape=target_shape, target_affine=affine)
resample_img(img, target_affine=affine)
with testing.write_tmp_imgs(img) as filename:
resample_img(filename, target_shape=target_shape, target_affine=affine)
# Missing parameter
pytest.raises(ValueError, resample_img, img, target_shape=target_shape)
# Invalid shape
pytest.raises(ValueError, resample_img, img, target_shape=(2, 3),
target_affine=affine)
# Invalid interpolation
interpolation = 'an_invalid_interpolation'
pattern = "interpolation must be either.+{0}".format(interpolation)
with pytest.raises(ValueError, match=pattern):
resample_img(img,
target_shape=target_shape,
target_affine=affine,
interpolation="an_invalid_interpolation"
)
# Noop
target_shape = shape[:3]
img_r = resample_img(img, copy=False)
assert img_r == img
img_r = resample_img(img, copy=True)
assert not np.may_share_memory(get_data(img_r), get_data(img))
np.testing.assert_almost_equal(get_data(img_r), get_data(img))
np.testing.assert_almost_equal(img_r.affine, img.affine)
img_r = resample_img(img, target_affine=affine, target_shape=target_shape,
copy=False)
assert img_r == img
img_r = resample_img(img, target_affine=affine, target_shape=target_shape,
copy=True)
assert not np.may_share_memory(get_data(img_r), get_data(img))
np.testing.assert_almost_equal(get_data(img_r), get_data(img))
np.testing.assert_almost_equal(img_r.affine, img.affine)
def test_new_img_like_mgz():
"""Check that new images can be generated with bool MGZ type
This is usually when computing masks using MGZ inputs, e.g.
when using plot_stap_map
"""
ref_img = nibabel.load(os.path.join(datadir, 'test.mgz'))
data = np.ones(get_data(ref_img).shape, dtype=bool)
affine = ref_img.affine
new_img_like(ref_img, data, affine, copy_header=False)
def test_threshold_img_copy():
"""Test the behavior of threshold_img's copy parameter."""
img_ones = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
# Check that copy does not mutate. It returns modified copy.
thresholded = threshold_img(img_ones, 2) # threshold 2 > 1
# Original img_ones should have all ones.
assert_array_equal(get_data(img_ones), np.ones((10, 10, 10, 10)))
# Thresholded should have all zeros.
assert_array_equal(get_data(thresholded), np.zeros((10, 10, 10, 10)))
# Check that not copying does mutate.
img_to_mutate = Nifti1Image(np.ones((10, 10, 10, 10)), np.eye(4))
thresholded = threshold_img(img_to_mutate, 2, copy=False)
# Check that original mutates
assert_array_equal(get_data(img_to_mutate), np.zeros((10, 10, 10, 10)))
# And that returned value is also thresholded.
assert_array_equal(get_data(img_to_mutate), get_data(thresholded))
def test_mask_4d():
# Dummy mask
mask = np.zeros((10, 10, 10), dtype=int)
mask[3:7, 3:7, 3:7] = 1
mask_bool = mask.astype(bool)
mask_img = Nifti1Image(mask, np.eye(4))
# Dummy data
data = np.zeros((10, 10, 10, 5), dtype=int)
data[..., 0] = 1
data[..., 1] = 2
data[..., 2] = 3
data_img_4d = Nifti1Image(data, np.eye(4))
data_imgs = [
index_img(data_img_4d, 0),
index_img(data_img_4d, 1),
index_img(data_img_4d, 2)
]
# check whether transform is indeed selecting niimgs subset
sample_mask = np.array([0, 2])
masker = NiftiMasker(mask_img=mask_img)
masker.fit()
data_trans = masker.transform(data_imgs, sample_mask=sample_mask)
data_trans_img = index_img(data_img_4d, sample_mask)
data_trans_direct = get_data(data_trans_img)[mask_bool, :]
data_trans_direct = np.swapaxes(data_trans_direct, 0, 1)
assert_array_equal(data_trans, data_trans_direct)
masker = NiftiMasker(mask_img=mask_img)
masker.fit()
data_trans2 = masker.transform(data_img_4d, sample_mask=sample_mask)
assert_array_equal(data_trans2, data_trans_direct)
diff_sample_mask = np.array([2, 4])
data_trans_img_diff = index_img(data_img_4d, diff_sample_mask)
data_trans_direct_diff = get_data(data_trans_img_diff)[mask_bool, :]
data_trans_direct_diff = np.swapaxes(data_trans_direct_diff, 0, 1)
masker = NiftiMasker(mask_img=mask_img)
masker.fit()
data_trans3 = masker.transform(data_img_4d, sample_mask=diff_sample_mask)
assert_array_equal(data_trans3, data_trans_direct_diff)
def test_resampling_result_axis_permutation():
# Transform real data using easily checkable transformations
# For now: axis permutations
# create a cuboid full of deterministic data, padded with one
# voxel thickness of zeros
core_shape = (3, 5, 4)
core_data = np.arange(np.prod(core_shape)).reshape(core_shape)
full_data_shape = np.array(core_shape) + 2
full_data = np.zeros(full_data_shape)
full_data[[slice(1, 1 + s) for s in core_shape]] = core_data
source_img = Nifti1Image(full_data, np.eye(4))
axis_permutations = [[0, 1, 2], [1, 0, 2], [2, 1, 0], [0, 2, 1]]
# check 3x3 transformation matrix
for ap in axis_permutations:
target_affine = np.eye(3)[ap]
resampled_img = resample_img(source_img, target_affine=target_affine)
resampled_data = get_data(resampled_img)
what_resampled_data_should_be = full_data.transpose(ap)
assert_array_almost_equal(resampled_data,
what_resampled_data_should_be)
# check 4x4 transformation matrix
offset = np.array([-2, 1, -3])
for ap in axis_permutations:
target_affine = np.eye(4)
target_affine[:3, :3] = np.eye(3)[ap]
target_affine[:3, 3] = offset
resampled_img = resample_img(source_img, target_affine=target_affine)
resampled_data = get_data(resampled_img)
offset_cropping = np.vstack(
[-offset[ap][np.newaxis, :],
np.zeros([1, 3])]).T.ravel().astype(int)
what_resampled_data_should_be = _pad_array(full_data.transpose(ap),
list(offset_cropping))
assert_array_almost_equal(resampled_data,
what_resampled_data_should_be)
def test_mean_img():
rng = np.random.RandomState(42)
data1 = np.zeros((5, 6, 7))
data2 = rng.rand(5, 6, 7)
data3 = rng.rand(5, 6, 7, 3)
affine = np.diag((4, 3, 2, 1))
img1 = nibabel.Nifti1Image(data1, affine=affine)
img2 = nibabel.Nifti1Image(data2, affine=affine)
img3 = nibabel.Nifti1Image(data3, affine=affine)
for imgs in ([img1, ],
[img1, img2],
[img2, img1, img2],
[img3, img1, img2], # Mixture of 4D and 3D images
):
arrays = list()
# Ground-truth:
for img in imgs:
img = get_data(img)
if img.ndim == 4:
img = np.mean(img, axis=-1)
arrays.append(img)
truth = np.mean(arrays, axis=0)
mean_img = image.mean_img(imgs)
assert_array_equal(mean_img.affine, affine)
assert_array_equal(get_data(mean_img), truth)
# Test with files
with testing.write_tmp_imgs(*imgs) as imgs:
mean_img = image.mean_img(imgs)
assert_array_equal(mean_img.affine, affine)
if X64:
assert_array_equal(get_data(mean_img), truth)
else:
# We don't really understand but arrays are not
# exactly equal on 32bit. Given that you can not do
# much real world data analysis with nilearn on a
# 32bit machine it is not worth investigating more
assert_allclose(get_data(mean_img), truth,
rtol=np.finfo(truth.dtype).resolution,
atol=0)
def test_downsample():
""" Test resampling with a 1/2 down-sampling affine.
"""
rng = np.random.RandomState(42)
shape = (6, 3, 6, 2)
data = rng.random_sample(shape)
affine = np.eye(4)
rot_img = resample_img(Nifti1Image(data, affine),
target_affine=2 * affine,
interpolation='nearest')
downsampled = data[::2, ::2, ::2, ...]
x, y, z = downsampled.shape[:3]
np.testing.assert_almost_equal(downsampled,
get_data(rot_img)[:x, :y, :z, ...])
rot_img_2 = resample_img(Nifti1Image(data, affine),
target_affine=2 * affine,
interpolation='nearest',
force_resample=True)
np.testing.assert_almost_equal(get_data(rot_img_2), get_data(rot_img))
# Test with non native endian data
# Test to check that if giving non native endian data as input should
# work as normal and expected to return the same output as above tests.
# Big endian data ('>f8')
for copy in [True, False]:
rot_img = resample_img(Nifti1Image(data.astype('>f8'), affine),
target_affine=2 * affine,
interpolation='nearest',
copy=copy)
np.testing.assert_almost_equal(downsampled,
get_data(rot_img)[:x, :y, :z, ...])
# Little endian data
for copy in [True, False]:
rot_img = resample_img(Nifti1Image(data.astype('<f8'), affine),
target_affine=2 * affine,
interpolation='nearest',
copy=copy)
np.testing.assert_almost_equal(downsampled,
get_data(rot_img)[:x, :y, :z, ...])
def test_compute_gray_matter_mask():
image = Nifti1Image(np.ones((9, 9, 9)), np.eye(4))
mask = compute_gray_matter_mask(image, threshold=-1)
mask1 = np.zeros((9, 9, 9))
mask1[2:-2, 2:-2, 2:-2] = 1
np.testing.assert_array_equal(mask1, get_data(mask))
# Check that we get a useful warning for empty masks
with pytest.warns(masking.MaskWarning):
compute_gray_matter_mask(image, threshold=1)
# Check that masks obtained from same FOV are the same
img1 = Nifti1Image(np.full((9, 9, 9), np.random.rand()), np.eye(4))
img2 = Nifti1Image(np.full((9, 9, 9), np.random.rand()), np.eye(4))
mask_img1 = compute_gray_matter_mask(img1)
mask_img2 = compute_gray_matter_mask(img2)
np.testing.assert_array_equal(get_data(mask_img1), get_data(mask_img2))
def make_timeseries_for_prf(bolds):
"""Takes a list of 4d nifti filenames, averages, cuts extra timepoints"""
for i, bold_file in enumerate(bolds):
img = load_img(bold_file)
data = get_data(img)
print(data.shape)
if i == 0:
all_data = np.empty((*data.shape, len(bolds)))
all_data[:, :, :, :, i] = data
mean_timeseries = np.average(all_data, -1)
return nib.Nifti1Image(mean_timeseries[:, :, :, :138], img.affine)
def _testdata_3d_for_resampling(img, binary):
"""Returns testing data for resampling tests.
Data can be binarize or not.
"""
data = get_data(img)
if binary:
data[data > 0] = 1
data[data < 0] = 0
affine = np.array([[1., -1., 0., 0.], [1., 1., 0., 0.], [0., 0., 1., 0.],
[0., 0., 0., 1.]])
return Nifti1Image(data, affine)
def test_compute_multi_gray_matter_mask():
# Check mask is correctly is correctly calculated
imgs = [Nifti1Image(np.random.rand(9, 9, 5), np.eye(4)),
Nifti1Image(np.random.rand(9, 9, 5), np.eye(4))]
masker = MultiNiftiMasker(mask_strategy='template')
masker.fit(imgs)
# Check that the order of the images does not change the output
masker2 = MultiNiftiMasker(mask_strategy='template')
masker2.fit(imgs[::-1])
mask = masker.mask_img_
mask2 = masker2.mask_img_
mask_ref = np.zeros((9, 9, 5))
mask_ref[2:7, 2:7, 2] = 1
np.testing.assert_array_equal(get_data(mask), mask_ref)
np.testing.assert_array_equal(get_data(mask2), mask_ref)
def test_fetch_atlas_pauli_2017():
data_dir = os.path.join(tst.tmpdir, 'pauli_2017')
data = atlas.fetch_atlas_pauli_2017('labels', data_dir)
assert_equal(len(data.labels), 16)
values = get_data(nibabel.load(data.maps))
assert_equal(len(np.unique(values)), 17)
data = atlas.fetch_atlas_pauli_2017('prob', data_dir)
assert_equal(nibabel.load(data.maps).shape[-1], 16)
def test_logistic_path_scores():
iris = load_iris()
X, y = iris.data, iris.target
_, mask = to_niimgs(X, [2, 2, 2])
mask = get_data(mask).astype(bool)
alphas = [1., .1, .01]
test_scores, best_w = logistic_path_scores(_graph_net_logistic, X, y, mask,
alphas, .5, np.arange(len(X)),
np.arange(len(X)), {})[:2]
test_scores = test_scores[0]
assert len(test_scores) == len(alphas)
assert X.shape[1] + 1 == len(best_w)
def load_mask(self, mask):
if mask is not None:
if 'mask' in mask or 'lh_' in mask or 'rh_' in mask:
fname = os.path.join(self.subj.masks, '%s.nii.gz' % (mask))
else:
fname = os.path.join(self.subj.masks, '%s_mask.nii.gz')
if not os.path.exists(fname):
fname = os.path.join(self.subj.masks, '%s.nii.gz' % (mask))
return get_data(fname)
def test_matrix_orientation():
"""Test if processing is performed along the correct axis."""
# the "step" kind generate heavyside-like signals for each voxel.
# all signals being identical, standardizing along the wrong axis
# would leave a null signal. Along the correct axis, the step remains.
fmri, mask = data_gen.generate_fake_fmri(shape=(40, 41, 42), kind="step")
masker = NiftiMasker(mask_img=mask, standardize=True, detrend=True)
timeseries = masker.fit_transform(fmri)
assert (timeseries.shape[0] == fmri.shape[3])
assert (timeseries.shape[1] == get_data(mask).sum())
std = timeseries.std(axis=0)
assert (std.shape[0] == timeseries.shape[1]) # paranoid
assert (not np.any(std < 0.1))
# Test inverse transform
masker = NiftiMasker(mask_img=mask, standardize=False, detrend=False)
masker.fit()
timeseries = masker.transform(fmri)
recovered = masker.inverse_transform(timeseries)
np.testing.assert_array_almost_equal(get_data(recovered), get_data(fmri))
def test_high_level_glm_with_paths():
shapes, rk = ((7, 8, 7, 15), (7, 8, 7, 14)), 3
with InTemporaryDirectory():
mask_file, fmri_files, design_files = write_fake_fmri_data_and_design(shapes, rk)
multi_session_model = FirstLevelModel(mask_img=None).fit(
fmri_files, design_matrices=design_files)
z_image = multi_session_model.compute_contrast(np.eye(rk)[1])
assert_array_equal(z_image.affine, load(mask_file).affine)
assert get_data(z_image).std() < 3.
# Delete objects attached to files to avoid WindowsError when deleting
# temporary directory (in Windows)
del z_image, fmri_files, multi_session_model
def test_reorder_img_non_native_endianness():
def _get_resampled_img(dtype):
data = np.ones((10, 10, 10), dtype=dtype)
data[3:7, 3:7, 3:7] = 2
affine = np.eye(4)
theta = math.pi / 6.
c = math.cos(theta)
s = math.sin(theta)
affine = np.array([[1, 0, 0, 0], [0, c, -s, 0], [0, s, c, 0],
[0, 0, 0, 1]])
img = Nifti1Image(data, affine)
return resample_img(img, target_affine=np.eye(4))
img_1 = _get_resampled_img('<f8')
img_2 = _get_resampled_img('>f8')
np.testing.assert_equal(get_data(img_1), get_data(img_2))
def test_get_clusters_table_not_modifying_stat_image():
shape = (9, 10, 11)
data = np.zeros(shape)
data[2:4, 5:7, 6:8] = 5.
data[0:3, 0:3, 0:3] = 6.
stat_img = nib.Nifti1Image(data, np.eye(4))
data_orig = get_data(stat_img).copy()
# test one cluster should be removed
clusters_table = get_clusters_table(
stat_img,
4,
cluster_threshold=10,
two_sided=True
)
assert np.allclose(data_orig, get_data(stat_img))
assert len(clusters_table) == 1
# test no clusters should be removed
stat_img = nib.Nifti1Image(data, np.eye(4))
clusters_table = get_clusters_table(
stat_img,
4,
cluster_threshold=7,
two_sided=False
)
assert np.allclose(data_orig, get_data(stat_img))
assert len(clusters_table) == 2
# test cluster threshold is None
stat_img = nib.Nifti1Image(data, np.eye(4))
clusters_table = get_clusters_table(
stat_img,
4,
cluster_threshold=None,
two_sided=False
)
assert np.allclose(data_orig, get_data(stat_img))
assert len(clusters_table) == 2
def test_clean_img():
rng = np.random.RandomState(0)
data = rng.randn(10, 10, 10, 100) + .5
data_flat = data.T.reshape(100, -1)
data_img = nibabel.Nifti1Image(data, np.eye(4))
pytest.raises(
ValueError, image.clean_img, data_img, t_r=None, low_pass=0.1)
data_img_ = image.clean_img(
data_img, detrend=True, standardize=False, low_pass=0.1, t_r=1.0)
data_flat_ = signal.clean(
data_flat, detrend=True, standardize=False, low_pass=0.1, t_r=1.0)
np.testing.assert_almost_equal(get_data(data_img_).T.reshape(100, -1),
data_flat_)
# if NANs
data[:, 9, 9] = np.nan
# if infinity
data[:, 5, 5] = np.inf
nan_img = nibabel.Nifti1Image(data, np.eye(4))
clean_im = image.clean_img(nan_img, ensure_finite=True)
assert np.any(np.isfinite(get_data(clean_im))), True
# test_clean_img_passing_nifti2image
data_img_nifti2 = nibabel.Nifti2Image(data, np.eye(4))
data_img_nifti2_ = image.clean_img(
data_img_nifti2, detrend=True, standardize=False, low_pass=0.1, t_r=1.0)
# if mask_img
img, mask_img = data_gen.generate_fake_fmri(shape=(10, 10, 10), length=10)
data_img_mask_ = image.clean_img(img, mask_img=mask_img)
# Checks that output with full mask and without is equal
data_img_ = image.clean_img(img)
np.testing.assert_almost_equal(get_data(data_img_),
get_data(data_img_mask_))
def test_view_img_on_surf():
img = _get_img()
surfaces = datasets.fetch_surf_fsaverage()
html = html_surface.view_img_on_surf(img, threshold='92.3%')
check_html(html)
html = html_surface.view_img_on_surf(img, threshold=0, surf_mesh=surfaces)
check_html(html)
html = html_surface.view_img_on_surf(img, threshold=.4, title="SOME_TITLE")
assert "SOME_TITLE" in html.html
check_html(html)
html = html_surface.view_img_on_surf(
img, threshold=.4, cmap='hot', black_bg=True)
check_html(html)
with pytest.raises(DimensionError):
html_surface.view_img_on_surf([img, img])
img_4d = image.new_img_like(img, get_data(img)[:, :, :, np.newaxis])
assert len(img_4d.shape) == 4
html = html_surface.view_img_on_surf(img, threshold='92.3%')
check_html(html)
np.clip(get_data(img), 0, None, out=get_data(img))
html = html_surface.view_img_on_surf(img, symmetric_cmap=False)
check_html(html)
