def test_is_image():
# tests for tests for image
img = load_image(anatfile)
yield assert_true(is_image(img))
class C(object): pass
yield assert_false(is_image(C()))
class C(object):
def __array__(self): pass
yield assert_false(is_image(C()))
class C(object):
coordmap = None
def __array__(self): pass
yield assert_true(is_image(img))
def test_mask():
mean_image = np.ones((9, 9))
mean_image[3:-3, 3:-3] = 10
mean_image[5, 5] = 100
mask1 = nnm.compute_mask(mean_image)
mask2 = nnm.compute_mask(mean_image, exclude_zeros=True)
# With an array with no zeros, exclude_zeros should not make
# any difference
assert_array_equal(mask1, mask2)
# Check that padding with zeros does not change the extracted mask
mean_image2 = np.zeros((30, 30))
mean_image2[:9, :9] = mean_image
mask3 = nnm.compute_mask(mean_image2, exclude_zeros=True)
assert_array_equal(mask1, mask3[:9, :9])
# However, without exclude_zeros, it does
mask3 = nnm.compute_mask(mean_image2)
assert_false(np.allclose(mask1, mask3[:9, :9]))
# check that opening is 2 by default
mask4 = nnm.compute_mask(mean_image, exclude_zeros=True, opening=2)
assert_array_equal(mask1, mask4)
# check that opening has an effect
mask5 = nnm.compute_mask(mean_image, exclude_zeros=True, opening=0)
assert_true(mask5.sum() > mask4.sum())
def test_mask():
mean_image = np.ones((9, 9))
mean_image[3:-3, 3:-3] = 10
mean_image[5, 5] = 100
mask1 = nnm.compute_mask(mean_image)
mask2 = nnm.compute_mask(mean_image, exclude_zeros=True)
# With an array with no zeros, exclude_zeros should not make
# any difference
assert_array_equal(mask1, mask2)
# Check that padding with zeros does not change the extracted mask
mean_image2 = np.zeros((30, 30))
mean_image2[:9, :9] = mean_image
mask3 = nnm.compute_mask(mean_image2, exclude_zeros=True)
assert_array_equal(mask1, mask3[:9, :9])
# However, without exclude_zeros, it does
mask3 = nnm.compute_mask(mean_image2)
assert_false(np.allclose(mask1, mask3[:9, :9]))
# check that opening is 2 by default
mask4 = nnm.compute_mask(mean_image, exclude_zeros=True, opening=2)
assert_array_equal(mask1, mask4)
# check that opening has an effect
mask5 = nnm.compute_mask(mean_image, exclude_zeros=True, opening=0)
assert_true(mask5.sum() > mask4.sum())
def test_save2b():
# A test to ensure that when a file is saved, the affine and the
# data agree. This image comes from a NIFTI file This example has
# a non-diagonal affine matrix for the spatial part, but is
# 'diagonal' for the space part. this should raise a warnings
# about 'non-diagonal' affine matrix
# make a 5x5 transformation
step = np.array([3.45,2.3,4.5,6.9])
A = np.random.standard_normal((4,4))
B = np.diag(list(step)+[1])
B[:4,:4] = A
shape = (13,5,7,3)
cmap = api.AffineTransform.from_params('ijkt', 'xyzt', B)
data = np.random.standard_normal(shape)
img = api.Image(data, cmap)
with InTemporaryDirectory():
save_image(img, TMP_FNAME)
img2 = load_image(TMP_FNAME)
assert_false(np.allclose(img.affine, img2.affine))
assert_array_almost_equal(img.affine[:3,:3], img2.affine[:3,:3])
assert_equal(img.shape, img2.shape)
assert_array_almost_equal(img2.get_data(), img.get_data())
del img2
