def test_bad_images():
setup()
g2, lag_steps = multi_tau_auto_corr(4, num_bufs, rois, img_stack)
# introduce bad images
bad_img_list = [3, 21, 35, 48]
# convert each bad image to np.nan array
images = bad_to_nan_gen(img_stack, bad_img_list)
# then use new images (including bad images)
g2_n, lag_steps_n = multi_tau_auto_corr(4, num_bufs, rois, images)
assert_array_almost_equal(g2[:, 0], g2_n[:, 0], decimal=3)
assert_array_almost_equal(g2[:, 1], g2_n[:, 1], decimal=3)
def test_bad_images():
setup()
g2, lag_steps = multi_tau_auto_corr(4, num_bufs,
rois, img_stack)
# introduce bad images
bad_img_list = [3, 21, 35, 48]
# convert each bad image to np.nan array
images = bad_to_nan_gen(img_stack, bad_img_list)
# then use new images (including bad images)
g2_n, lag_steps_n = multi_tau_auto_corr(4, num_bufs,
rois, images)
assert_array_almost_equal(g2[:, 0], g2_n[:, 0], decimal=3)
assert_array_almost_equal(g2[:, 1], g2_n[:, 1], decimal=3)
def test_bad_to_nan_gen():
xdim = 2
ydim = 2
stack_size = 5
img_stack = np.random.randint(1, 3, (stack_size, xdim, ydim))
bad_list = [1, 3]
img = mask.bad_to_nan_gen(img_stack, bad_list)
y = []
for im in img:
y.append(im)
assert np.isnan(np.asarray(y)[1]).all()
assert np.isnan(np.asarray(y)[3]).all()
assert not np.isnan(np.asarray(y)[4]).all()
def test_bad_to_nan_gen():
xdim = 2
ydim = 2
stack_size = 5
img_stack = np.random.randint(1, 3, (stack_size, xdim, ydim))
bad_list = [1, 3]
img = mask.bad_to_nan_gen(img_stack, bad_list)
y = []
for im in img:
y.append(im)
assert np.isnan(np.asarray(y)[1]).all()
assert np.isnan(np.asarray(y)[3]).all()
assert not np.isnan(np.asarray(y)[4]).all()
def test_xsvs():
images = []
for i in range(5):
int_array = np.tril((i + 2) * np.ones(10))
int_array[int_array == 0] = (i + 1)
images.append(int_array)
images_sets = [
np.asarray(images),
]
roi_data = np.array(([4, 2, 2, 2], [0, 5, 4, 4]), dtype=np.int64)
label_array = roi.rectangles(roi_data, shape=images[0].shape)
prob_k_all, std = xsvs.xsvs(images_sets,
label_array,
timebin_num=2,
number_of_img=5,
max_cts=6)
assert_array_almost_equal(prob_k_all[0, 0],
np.array([0., 0., 0.2, 0.2, 0.4]))
assert_array_almost_equal(prob_k_all[0, 1],
np.array([0., 0.2, 0.2, 0.2, 0.4]))
imgs = []
for i in range(6):
int_array = np.tril((i + 2) * np.ones(10))
int_array[int_array == 0] = (i + 1)
imgs.append(int_array)
# testing for bad images
bad_list = [5]
# convert each bad image to np.nan array
images1 = mask.bad_to_nan_gen(imgs, bad_list)
new_prob_k, new_std = xsvs.xsvs((images1, ),
label_array,
timebin_num=2,
number_of_img=5,
max_cts=6)
assert_array_almost_equal(new_prob_k[0, 0],
np.array([0., 0., 0.2, 0.2, 0.4]))
assert_array_almost_equal(new_prob_k[0, 1],
np.array([0., 0.2, 0.2, 0.2, 0.4]))
def test_xsvs():
images = []
for i in range(5):
int_array = np.tril((i + 2) * np.ones(10))
int_array[int_array == 0] = (i + 1)
images.append(int_array)
images_sets = [np.asarray(images), ]
roi_data = np.array(([4, 2, 2, 2], [0, 5, 4, 4]), dtype=np.int64)
label_array = roi.rectangles(roi_data, shape=images[0].shape)
prob_k_all, std = xsvs.xsvs(images_sets, label_array, timebin_num=2,
number_of_img=5, max_cts=6)
assert_array_almost_equal(prob_k_all[0, 0],
np.array([0., 0., 0.2, 0.2, 0.4]))
assert_array_almost_equal(prob_k_all[0, 1],
np.array([0., 0.2, 0.2, 0.2, 0.4]))
imgs = []
for i in range(6):
int_array = np.tril((i + 2) * np.ones(10))
int_array[int_array == 0] = (i + 1)
imgs.append(int_array)
# testing for bad images
bad_list = [5]
# convert each bad image to np.nan array
images1 = mask.bad_to_nan_gen(imgs, bad_list)
new_prob_k, new_std = xsvs.xsvs((images1, ), label_array,
timebin_num=2, number_of_img=5,
max_cts=6)
assert_array_almost_equal(new_prob_k[0, 0],
np.array([0., 0., 0.2, 0.2, 0.4]))
assert_array_almost_equal(new_prob_k[0, 1],
np.array([0., 0.2, 0.2, 0.2, 0.4]))
