def test_inverse_transform_single_nifti_image():
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
methods = ['kmeans', 'ward', 'complete', 'average', 'rena']
for method in methods:
parcellate = Parcellations(method=method, n_parcels=5)
# Fit
parcellate.fit(fmri_img)
assert parcellate.labels_img_ is not None
# Transform
fmri_reduced = parcellate.transform(fmri_img)
assert isinstance(fmri_reduced, np.ndarray)
# Shape matching with (scans, regions)
assert fmri_reduced.shape, (10, 5)
# Inverse transform
fmri_compressed = parcellate.inverse_transform(fmri_reduced)
# A single Nifti image for single subject input
assert isinstance(fmri_compressed, nibabel.Nifti1Image)
# returns shape of fmri_img
assert fmri_compressed.shape, (10, 11, 12, 10)
# fmri_reduced in a list
fmri_compressed = parcellate.inverse_transform([fmri_reduced])
# A single Nifti image for single subject input
assert isinstance(fmri_compressed, nibabel.Nifti1Image)
# returns shape of fmri_img
assert fmri_compressed.shape, (10, 11, 12, 10)
def test_inverse_transform_single_nifti_image():
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
methods = ['kmeans', 'ward', 'complete', 'average']
for method in methods:
parcellate = Parcellations(method=method, n_parcels=5)
# Fit
parcellate.fit(fmri_img)
assert_true(parcellate.labels_img_ is not None)
# Transform
fmri_reduced = parcellate.transform(fmri_img)
assert_true(isinstance(fmri_reduced, np.ndarray))
# Shape matching with (scans, regions)
assert_true(fmri_reduced.shape, (10, 5))
# Inverse transform
fmri_compressed = parcellate.inverse_transform(fmri_reduced)
# A single Nifti image for single subject input
assert_true(isinstance(fmri_compressed, nibabel.Nifti1Image))
# returns shape of fmri_img
assert_true(fmri_compressed.shape, (10, 11, 12, 10))
# fmri_reduced in a list
fmri_compressed = parcellate.inverse_transform([fmri_reduced])
# A single Nifti image for single subject input
assert_true(isinstance(fmri_compressed, nibabel.Nifti1Image))
# returns shape of fmri_img
assert_true(fmri_compressed.shape, (10, 11, 12, 10))
def test_inverse_transform_single_nifti_image(method, n_parcel, test_image_2):
parcellate = Parcellations(method=method, n_parcels=n_parcel)
parcellate.fit(test_image_2)
assert parcellate.labels_img_ is not None
fmri_reduced = parcellate.transform(test_image_2)
assert isinstance(fmri_reduced, np.ndarray)
# Shape matching with (scans, regions)
assert fmri_reduced.shape == (10, n_parcel)
fmri_compressed = parcellate.inverse_transform(fmri_reduced)
# A single Nifti image for single subject input
assert isinstance(fmri_compressed, nibabel.Nifti1Image)
# returns shape of fmri_img
assert fmri_compressed.shape == test_image_2.shape
# fmri_reduced in a list
fmri_compressed = parcellate.inverse_transform([fmri_reduced])
# A single Nifti image for single subject input
assert isinstance(fmri_compressed, nibabel.Nifti1Image)
# returns shape of fmri_img
assert fmri_compressed.shape == test_image_2.shape
def test_transform_3d_input_images():
# test list of 3D images
data = np.ones((10, 11, 12))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
img = nibabel.Nifti1Image(data, affine=np.eye(4))
imgs = [img] * 3
parcellate = Parcellations(method='ward', n_parcels=20)
X = parcellate.fit_transform(imgs)
assert isinstance(X, list)
# (number of samples, number of features)
assert np.concatenate(X).shape == (3, 20)
# inverse transform
imgs_ = parcellate.inverse_transform(X)
assert isinstance(imgs_, list)
# test single 3D image
X = parcellate.fit_transform(imgs[0])
assert isinstance(X, np.ndarray)
assert X.shape == (1, 20)
def test_transform_3d_input_images():
# test list of 3D images
data = np.ones((10, 11, 12))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
img = nibabel.Nifti1Image(data, affine=np.eye(4))
# list of 3
imgs = [img, img, img]
parcellate = Parcellations(method='ward', n_parcels=20)
X = parcellate.fit_transform(imgs)
assert_true(isinstance(X, list))
# (number of samples, number of features)
assert_equal(np.concatenate(X).shape, (3, 20))
# inverse transform
imgs_ = parcellate.inverse_transform(X)
assert_true(isinstance(imgs_, list))
# test single 3D image
X = parcellate.fit_transform(imgs[0])
assert_true(isinstance(X, np.ndarray))
assert_equal(X.shape, (1, 20))