def test_check_parameters_transform(test_image_2):
rng = np.random.RandomState(42)
# single confound
confounds = rng.standard_normal(size=(10, 3))
# Tests to check whether imgs, confounds returned are
# list or not. Pre-check in parameters to work for list
# of multi images and multi confounds
imgs, confounds, single_subject = _check_parameters_transform(
test_image_2, confounds)
assert isinstance(imgs, (list, tuple))
assert isinstance(confounds, (list, tuple))
assert single_subject, True
# confounds as pandas DataFrame
imgs, confounds, single_subject = _check_parameters_transform(
test_image_2, pd.DataFrame(np.array(confounds)[0]))
assert isinstance(confounds, (list, tuple))
# multi images
fmri_imgs = [test_image_2] * 3
confounds_list = [confounds] * 3
imgs, confounds, _ = _check_parameters_transform(fmri_imgs, confounds_list)
assert imgs == fmri_imgs
assert confounds_list == confounds
# Test the error when length of images and confounds are not same
msg = ("Number of confounds given does not match with the "
"given number of images")
not_match_confounds_list = [confounds] * 2
with pytest.raises(ValueError, match=msg):
_check_parameters_transform(fmri_imgs, not_match_confounds_list)
def test_check_parameters_transform():
rng = np.random.RandomState(0)
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
# single image
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
# single confound
confounds = rng.randn(*(10, 3))
# Tests to check whether imgs, confounds returned are
# list or not. Pre-check in parameters to work for list
# of multi images and multi confounds
imgs, confounds, single_subject = _check_parameters_transform(
fmri_img, confounds)
assert_true(isinstance(imgs, (list, tuple)))
assert_true(isinstance(confounds, (list, tuple)))
assert_true(single_subject, True)
# multi images
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds_list = [confounds, confounds, confounds]
imgs, confounds, _ = _check_parameters_transform(fmri_imgs, confounds_list)
assert_equal(imgs, fmri_imgs)
assert_equal(confounds_list, confounds)
# Test the error when length of images and confounds are not same
msg = ("Number of confounds given does not match with the "
"given number of images")
not_match_confounds_list = [confounds, confounds]
assert_raises_regex(ValueError, msg, _check_parameters_transform,
fmri_imgs, not_match_confounds_list)
def test_check_parameters_transform():
rng = np.random.RandomState(0)
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
# single image
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
# single confound
confounds = rng.randn(*(10, 3))
# Tests to check whether imgs, confounds returned are
# list or not. Pre-check in parameters to work for list
# of multi images and multi confounds
imgs, confounds, single_subject = _check_parameters_transform(fmri_img,
confounds)
assert_true(isinstance(imgs, (list, tuple)))
assert_true(isinstance(confounds, (list, tuple)))
assert_true(single_subject, True)
# multi images
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds_list = [confounds, confounds, confounds]
imgs, confounds, _ = _check_parameters_transform(fmri_imgs, confounds_list)
assert_equal(imgs, fmri_imgs)
assert_equal(confounds_list, confounds)
# Test the error when length of images and confounds are not same
msg = ("Number of confounds given does not match with the "
"given number of images")
not_match_confounds_list = [confounds, confounds]
assert_raises_regex(ValueError, msg, _check_parameters_transform,
fmri_imgs, not_match_confounds_list)
def test_check_parameters_transform():
rng = np.random.RandomState(42)
data = np.ones((10, 11, 12, 10))
data[6, 7, 8] = 2
data[9, 10, 11] = 3
# single image
fmri_img = nibabel.Nifti1Image(data, affine=np.eye(4))
# single confound
confounds = rng.standard_normal(size=(10, 3))
# Tests to check whether imgs, confounds returned are
# list or not. Pre-check in parameters to work for list
# of multi images and multi confounds
imgs, confounds, single_subject = _check_parameters_transform(
fmri_img, confounds)
assert isinstance(imgs, (list, tuple))
assert isinstance(confounds, (list, tuple))
assert single_subject, True
# confounds as pandas DataFrame
imgs, confounds, single_subject = _check_parameters_transform(
fmri_img, pd.DataFrame(np.array(confounds)[0]))
assert isinstance(confounds, (list, tuple))
# multi images
fmri_imgs = [fmri_img, fmri_img, fmri_img]
confounds_list = [confounds, confounds, confounds]
imgs, confounds, _ = _check_parameters_transform(fmri_imgs, confounds_list)
assert imgs == fmri_imgs
assert confounds_list == confounds
# Test the error when length of images and confounds are not same
msg = ("Number of confounds given does not match with the "
"given number of images")
not_match_confounds_list = [confounds, confounds]
with pytest.raises(ValueError, match=msg):
_check_parameters_transform(fmri_imgs, not_match_confounds_list)