def test_circular_sectors(self):
"""Test circular sectors method in segmentation.
"""
# create data for image 2 channels
channels = ['ch0', 'ch1']
data = np.stack(
(
# channel 0
np.arange(36, dtype='float').reshape(6, 6),
# this is the matrix:
#np.array([[ 0, 1, 2, 3, 4, 5],
# [ 6, 7, 8, 9, 10, 11],
# [12, 13, 14, 15, 16, 17],
# [18, 19, 20, 21, 22, 23],
# [24, 25, 26, 27, 28, 29],
# [30, 31, 32, 33, 34, 35]], dtype='float')
# channel 1
np.array([[0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0], [0, 0, 3, 0, 0, 2]],
dtype='float'),
),
axis=2)
# assume labels are for cell ID 1, such as with label image:
# np.array([
# [1, 1, 1, 1, 1, 0],
# [1, 1, 1, 1, 1, 0],
# [1, 1, 1, 1, 1, 0],
# [1, 1, 1, 1, 1, 0],
# [1, 1, 1, 1, 1, 0],
# [0, 0, 0, 0, 0, 0]])
# indices of the pixels of the cell
x = np.arange(5)
y = x
x_inds, y_inds = np.meshgrid(x, y, indexing='ij')
inds = (y_inds.flatten(), x_inds.flatten())
# sum within sectors and calculate geometric mean for each channel
secs = []
for i in range(len(channels)):
sec1 = data[2][2][i] + data[2][3][i] + data[2][4][i] + data[3][4][i]
sec2 = data[3][3][i] + data[4][3][i] + data[4][4][i]
sec3 = data[3][2][i] + data[4][2][i] + data[4][1][i]
sec4 = data[3][1][i] + data[4][0][i] + data[3][0][i]
sec5 = data[2][1][i] + data[2][0][i] + data[1][0][i]
sec6 = data[1][1][i] + data[0][0][i] + data[0][1][i]
sec7 = data[1][2][i] + data[0][2][i] + data[0][3][i]
sec8 = data[1][3][i] + data[0][4][i] + data[1][4][i]
secs_geom_mean = np.power(sec1 * sec2 * sec3 * sec4 * \
sec5 * sec6 * sec7 * sec8, 1/8)
secs.append(secs_geom_mean)
expected = np.array(secs)
# test the function
image = mi.MibiImage(data, channels)
circ_secs = segmentation._circular_sectors_mean(inds,
image,
num_sectors=8)
assert_array_equal(circ_secs, expected)
def test_circular_sectors_small_cell(self):
"""Test small cell with empty sectors.
"""
# create data for image 2 channels
channels = ['ch0', 'ch1']
data = np.stack(
(
# channel 0
np.arange(16, dtype='float').reshape(4, 4),
# this is the matrix:
#np.array([[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11],
# [12, 13, 14, 15]], dtype='float')
# channel 1
np.array(
[[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 0]],
dtype='float'),
),
axis=2)
# assume labels are for cell ID 1, such as with label image:
# np.array([
# [0, 1, 1, 0],
# [0, 1, 1, 0],
# [0, 0, 0, 0],
# [0, 0, 0, 0]])
# indices of the pixels of the cell
inds = ((0, 0, 1, 1), (1, 2, 1, 2))
# sum within sectors and calculate geometric mean for each channel
secs = []
for i in range(len(channels)):
sec1 = 1 # empty sector
sec2 = data[1][2][i]
sec3 = 1 # empty sector
sec4 = data[1][1][i]
sec5 = 1 # empty sector
sec6 = data[0][1][i]
sec7 = 1 # empty sector
sec8 = data[0][2][i]
secs_geom_mean = np.power(sec1 * sec2 * sec3 * sec4 * \
sec5 * sec6 * sec7 * sec8, 1/8)
secs.append(secs_geom_mean)
expected = np.array(secs)
# test the function
image = mi.MibiImage(data, channels)
circ_secs = segmentation._circular_sectors_mean(inds,
image,
num_sectors=8)
assert_array_equal(circ_secs, expected)
def test_quadrant_mean(self):
data = np.stack((
np.array([[3, 2, 4], [1, 1, 3], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 0, 0], [0, 0, 2]]),
),
axis=2)
# assume labels are for cell ID 1, such as with label image:
# np.array([
# [0, 1, 1],
# [1, 1, 1],
# [0, 0, 0]
# ])
inds = ((0, 0, 1, 1, 1), (1, 2, 0, 1, 2))
quads_channel_0 = np.power(2 * 4 * (1 + 1) * 3, 1 / 4) # pylint: disable=assignment-from-no-return
quads_channel_1 = np.power(0 * 1 * (0 + 0) * 0, 1 / 4) # pylint: disable=assignment-from-no-return
expected = np.array((quads_channel_0, quads_channel_1))
image = mi.MibiImage(data, ['1', '2'])
assert_array_equal(
segmentation._circular_sectors_mean(inds, image, num_sectors=4),
expected)
def test_extract_cell_dataframe(self):
data = np.stack((
np.array([[3, 2, 4, 0], [1, 1, 3, 1], [0, 0, 1, 1], [5, 0, 3, 1]]),
np.array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 2, 0], [5, 0, 0, 0]]),
),
axis=2)
cell_labels = np.array([[0, 1, 1, 0], [1, 1, 3, 3], [0, 0, 3, 3],
[0, 0, 3, 3]])
image = mi.MibiImage(data, ['1', '2'])
labels = [1, 3]
areas = [4, 6]
x_centroids = [1, 2]
y_centroids = [0, 2]
first_total = [8, 10]
second_total = [1, 3]
# Check coords and areas only
expected_from_labels = pd.DataFrame(
np.array([areas, x_centroids, y_centroids]).T,
columns=['area', 'x_centroid', 'y_centroid'],
index=pd.Index(labels, name='label'))
pdt.assert_frame_equal(
segmentation.extract_cell_dataframe(cell_labels),
expected_from_labels)
# Check mode 'total'
expected_from_total = pd.DataFrame(
np.array([first_total, second_total]).T,
columns=['1', '2'],
index=pd.Index(labels, name='label'))
pdt.assert_frame_equal(
segmentation.extract_cell_dataframe(cell_labels, image),
pd.concat((expected_from_labels, expected_from_total), axis=1))
# Check mode 'quadrant'
quads = []
for label in labels:
inds = np.nonzero(cell_labels == label)
quads.append(
segmentation._circular_sectors_mean(inds, image,
num_sectors=4))
expected_from_quadrants = pd.DataFrame(np.array(quads),
columns=['1', '2'],
index=pd.Index(labels,
name='label'))
pdt.assert_frame_equal(
segmentation.extract_cell_dataframe(cell_labels,
image,
mode='quadrant'),
pd.concat((expected_from_labels, expected_from_quadrants), axis=1))
# Check mode 'circular_sectors'
secs = []
for label in labels:
inds = np.nonzero(cell_labels == label)
num_sectors = 8
secs.append(
segmentation._circular_sectors_mean(inds, image, num_sectors))
expected_from_circular_sectors = pd.DataFrame(
np.array(secs),
columns=['1', '2'],
index=pd.Index(labels, name='label'))
pdt.assert_frame_equal(
segmentation.extract_cell_dataframe(cell_labels,
image,
mode='circular_sectors',
num_sectors=num_sectors),
pd.concat((expected_from_labels, expected_from_circular_sectors),
axis=1))