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))
data_img_ = image.clean_img(data_img,
detrend=True,
standardize=False,
low_pass=0.1)
data_flat_ = signal.clean(data_flat,
detrend=True,
standardize=False,
low_pass=0.1)
np.testing.assert_almost_equal(data_img_.get_data().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_true(np.any(np.isfinite(clean_im.get_data())), 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)
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))
assert_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(data_img_.get_data().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_true(np.any(np.isfinite(clean_im.get_data())), 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)
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))
assert_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(data_img_.get_data().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_true(np.any(np.isfinite(clean_im.get_data())), 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(data_img_.get_data(),
data_img_mask_.get_data())
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))
assert_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(data_img_.get_data().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_true(np.any(np.isfinite(clean_im.get_data())), 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(data_img_.get_data(),
data_img_mask_.get_data())
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))
data_img_ = image.clean_img(
data_img, detrend=True, standardize=False, low_pass=0.1)
data_flat_ = signal.clean(
data_flat, detrend=True, standardize=False, low_pass=0.1)
np.testing.assert_almost_equal(data_img_.get_data().T.reshape(100, -1),
data_flat_)
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))
data_img_ = image.clean_img(data_img,
detrend=True,
standardize=False,
low_pass=0.1)
data_flat_ = signal.clean(data_flat,
detrend=True,
standardize=False,
low_pass=0.1)
np.testing.assert_almost_equal(data_img_.get_data().T.reshape(100, -1),
data_flat_)
