def test_parcellations_transform_single_nifti_image():
# Test with NiftiLabelsMasker extraction of timeseries data
# after building a parcellations image
# Here, data has ones. zeros will be considered as background labels
# not foreground labels
data = np.ones((10, 11, 12, 8))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
parcels = 5
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
for method in ['kmeans', 'ward', 'complete', 'average']:
parcellator = Parcellations(method=method, n_parcels=parcels)
parcellator.fit(fmri_img)
# transform to signals
signals = parcellator.transform(fmri_img)
# Test if the signals extracted are of same shape as inputs
# Here, we simply return numpy array for single subject input
assert_equal(signals.shape, (fmri_img.shape[3], parcels))
# Test for single subject but in a list.
signals = parcellator.transform([fmri_img])
assert_equal(signals.shape, (fmri_img.shape[3], parcels))
def test_parcellations_transform_single_nifti_image():
# Test with NiftiLabelsMasker extraction of timeseries data
# after building a parcellations image
# Here, data has ones. zeros will be considered as background labels
# not foreground labels
data = np.ones((10, 11, 12, 8))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
parcels = 5
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
parcellator = Parcellations(method=method, n_parcels=parcels)
parcellator.fit(fmri_img)
# transform to signals
signals = parcellator.transform(fmri_img)
# Test if the signals extracted are of same shape as inputs
# Here, we simply return numpy array for single subject input
assert signals.shape == (fmri_img.shape[3], parcels)
# Test for single subject but in a list.
signals = parcellator.transform([fmri_img])
assert signals.shape == (fmri_img.shape[3], parcels)
def test_parcellations_transform_single_nifti_image(method, n_parcel,
test_image_2):
"""Test with NiftiLabelsMasker extraction of timeseries data
after building a parcellations image."""
parcellator = Parcellations(method=method, n_parcels=n_parcel)
parcellator.fit(test_image_2)
# transform to signals
signals = parcellator.transform(test_image_2)
# Test if the signals extracted are of same shape as inputs
# Here, we simply return numpy array for single subject input
assert signals.shape == (test_image_2.shape[3], n_parcel)
# Test for single subject but in a list.
signals = parcellator.transform([test_image_2])
assert signals.shape == (test_image_2.shape[3], n_parcel)
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_parcellations_transform_with_multi_confounds_multi_images(
method, n_parcel, test_image_2): # noqa: E501
rng = np.random.RandomState(42)
fmri_imgs = [test_image_2] * 3
confounds = rng.standard_normal(size=(10, 3))
confounds_list = [confounds] * 3
parcellator = Parcellations(method=method, n_parcels=n_parcel)
parcellator.fit(fmri_imgs)
signals = parcellator.transform(fmri_imgs, confounds=confounds_list)
assert isinstance(signals, list)
# n_parcels=5, length of data=10
assert signals[0].shape == (10, n_parcel)
def test_parcellations_transform_multi_nifti_images(method, n_parcel,
test_image_2):
fmri_imgs = [test_image_2] * 3
parcellator = Parcellations(method=method, n_parcels=n_parcel)
parcellator.fit(fmri_imgs)
# transform multi images to signals.
# In return, we have length equal to the number of images
signals = parcellator.transform(fmri_imgs)
assert signals[0].shape == (test_image_2.shape[3], n_parcel)
assert signals[1].shape == (test_image_2.shape[3], n_parcel)
assert signals[2].shape == (test_image_2.shape[3], n_parcel)
assert len(signals) == len(fmri_imgs)
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_parcellations_transform_with_multi_confounds_multi_images():
rng = np.random.RandomState(42)
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))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds = rng.standard_normal(size=(10, 3))
confounds_list = (confounds, confounds, confounds)
for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
parcellator = Parcellations(method=method, n_parcels=5)
parcellator.fit(fmri_imgs)
signals = parcellator.transform(fmri_imgs, confounds=confounds_list)
assert isinstance(signals, list)
# n_parcels=5, length of data=10
assert signals[0].shape == (10, 5)
def test_parcellations_transform_multi_nifti_images():
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
parcels = 5
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
for method in ['kmeans', 'ward', 'complete', 'average', 'rena']:
parcellator = Parcellations(method=method, n_parcels=parcels)
parcellator.fit(fmri_imgs)
# transform multi images to signals. In return, we have length
# equal to the number of images
signals = parcellator.transform(fmri_imgs)
assert signals[0].shape == (fmri_img.shape[3], parcels)
assert signals[1].shape == (fmri_img.shape[3], parcels)
assert signals[2].shape == (fmri_img.shape[3], parcels)
assert len(signals) == len(fmri_imgs)
def test_parcellations_transform_multi_nifti_images():
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
parcels = 5
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
for method in ['kmeans', 'ward', 'complete', 'average']:
parcellator = Parcellations(method=method, n_parcels=parcels)
parcellator.fit(fmri_imgs)
# transform multi images to signals. In return, we have length
# equal to the number of images
signals = parcellator.transform(fmri_imgs)
assert_equal(signals[0].shape, (fmri_img.shape[3], parcels))
assert_equal(signals[1].shape, (fmri_img.shape[3], parcels))
assert_equal(signals[2].shape, (fmri_img.shape[3], parcels))
assert_equal(len(signals), len(fmri_imgs))
def test_parcellations_transform_with_multi_confounds_multi_images():
rng = np.random.RandomState(0)
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))
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds = rng.randn(*(10, 3))
confounds_list = (confounds, confounds, confounds)
for method in ['kmeans', 'ward', 'complete', 'average']:
parcellator = Parcellations(method=method, n_parcels=5)
parcellator.fit(fmri_imgs)
signals = parcellator.transform(fmri_imgs,
confounds=confounds_list)
assert_true(isinstance(signals, list))
# n_parcels=5, length of data=10
assert_equal(signals[0].shape, (10, 5))