def test_segmented_rings():
center = (75, 75)
img_dim = (150, 140)
first_q = 5
delta_q = 5
num_rings = 4 # number of Q rings
slicing = 4
edges = roi.ring_edges(first_q, width=delta_q, spacing=4,
num_rings=num_rings)
print("edges", edges)
label_array = roi.segmented_rings(edges, slicing, center,
img_dim, offset_angle=0)
print("label_array for segmented_rings", label_array)
# Did we draw the right number of ROIs?
label_list = np.unique(label_array.ravel())
actual_num_labels = len(label_list) - 1
num_labels = num_rings * slicing
assert_equal(actual_num_labels, num_labels)
# Did we draw the right ROIs? (1-16 with some zeros around too)
assert_array_equal(label_list, np.arange(num_labels + 1))
# A brittle test to make sure the exactly number of pixels per label
# is never accidentally changed:
# number of pixels per ROI
num_pixels = np.bincount(label_array.ravel())
expected_num_pixels = [18372, 59, 59, 59, 59, 129, 129, 129,
129, 200, 200, 200, 200, 269, 269, 269, 269]
assert_array_equal(num_pixels, expected_num_pixels)
def test_segmented_rings():
center = (75, 75)
img_dim = (150, 140)
first_q = 5
delta_q = 5
num_rings = 4 # number of Q rings
slicing = 4
edges = roi.ring_edges(first_q, width=delta_q, spacing=4,
num_rings=num_rings)
print("edges", edges)
label_array = roi.segmented_rings(edges, slicing, center,
img_dim, offset_angle=0)
print("label_array for segmented_rings", label_array)
# Did we draw the right number of ROIs?
label_list = np.unique(label_array.ravel())
actual_num_labels = len(label_list) - 1
num_labels = num_rings * slicing
assert_equal(actual_num_labels, num_labels)
# Did we draw the right ROIs? (1-16 with some zeros around too)
assert_array_equal(label_list, np.arange(num_labels + 1))
# A brittle test to make sure the exactly number of pixels per label
# is never accidentally changed:
# number of pixels per ROI
num_pixels = np.bincount(label_array.ravel())
expected_num_pixels = [18372, 59, 59, 59, 59, 129, 129, 129,
129, 200, 200, 200, 200, 269, 269, 269, 269]
assert_array_equal(num_pixels, expected_num_pixels)
def testCrossCorrelator2d():
''' Test the 2D case of the cross correlator.
With non-binary labels.
'''
np.random.seed(123)
# test 2D data
Npoints2 = 10
x2 = np.linspace(-10, 10, Npoints2)
X, Y = np.meshgrid(x2, x2)
Z = np.random.random((Npoints2, Npoints2))
np.random.seed(123)
sigma = .2
# purposely have sparsely filled values (with lots of zeros)
# place peaks in random positions
peak_positions = (np.random.random((2, 10)) - .5) * 20
for peak_position in peak_positions:
Z += np.exp(-((X - peak_position[0])**2 + (Y - peak_position[1])**2) /
2. / sigma**2)
mask_2D = np.ones_like(Z)
mask_2D[1:2, 1:2] = 0
mask_2D[7:9, 4:6] = 0
mask_2D[1:2, 9:] = 0
# Compute with segmented rings
edges = ring_edges(1, 3, num_rings=2)
segments = 5
x0, y0 = np.array(mask_2D.shape) // 2
maskids = segmented_rings(edges, segments, (y0, x0), mask_2D.shape)
cc2D_ids = CrossCorrelator(mask_2D.shape, mask=maskids)
cc2D_ids_symavg = CrossCorrelator(mask_2D.shape,
mask=maskids,
normalization='symavg')
# 10 ids
assert_equal(cc2D_ids.nids, 10)
ycorr_ids_2D = cc2D_ids(Z)
ycorr_ids_2D_symavg = cc2D_ids_symavg(Z)
index = 0
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([1.22195059, 1.08685771, 1.43246508, 1.08685771, 1.22195059]))
index = 1
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([1.24324268, 0.80748997, 1.35790022, 0.80748997, 1.24324268]))
index = 0
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([0.84532695, 1.16405848, 1.43246508, 1.16405848, 0.84532695]))
index = 1
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([0.94823482, 0.8629459, 1.35790022, 0.8629459, 0.94823482]))
def testCrossCorrelator2d():
''' Test the 2D case of the cross correlator.
With non-binary labels.
'''
np.random.seed(123)
# test 2D data
Npoints2 = 10
x2 = np.linspace(-10, 10, Npoints2)
X, Y = np.meshgrid(x2, x2)
Z = np.random.random((Npoints2, Npoints2))
np.random.seed(123)
sigma = .2
# purposely have sparsely filled values (with lots of zeros)
# place peaks in random positions
peak_positions = (np.random.random((2, 10))-.5)*20
for peak_position in peak_positions:
Z += np.exp(-((X - peak_position[0])**2 +
(Y - peak_position[1])**2)/2./sigma**2)
mask_2D = np.ones_like(Z)
mask_2D[1:2, 1:2] = 0
mask_2D[7:9, 4:6] = 0
mask_2D[1:2, 9:] = 0
# Compute with segmented rings
edges = ring_edges(1, 3, num_rings=2)
segments = 5
x0, y0 = np.array(mask_2D.shape)//2
maskids = segmented_rings(edges, segments, (y0, x0), mask_2D.shape)
cc2D_ids = CrossCorrelator(mask_2D.shape, mask=maskids)
cc2D_ids_symavg = CrossCorrelator(mask_2D.shape, mask=maskids,
normalization='symavg')
# 10 ids
assert_equal(cc2D_ids.nids, 10)
ycorr_ids_2D = cc2D_ids(Z)
ycorr_ids_2D_symavg = cc2D_ids_symavg(Z)
index = 0
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([1.22195059, 1.08685771,
1.43246508, 1.08685771, 1.22195059
])
)
index = 1
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([1.24324268, 0.80748997,
1.35790022, 0.80748997, 1.24324268
])
)
index = 0
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D_symavg[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([0.84532695, 1.16405848, 1.43246508,
1.16405848, 0.84532695])
)
index = 1
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D_symavg[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([0.94823482, 0.8629459, 1.35790022,
0.8629459, 0.94823482])
)