def test_image_stack_correlation():
num_levels = 1
num_bufs = 2 # must be even
coins = data.camera()
coins_stack = []
for i in range(4):
coins_stack.append(coins)
coins_mesh = np.zeros_like(coins)
coins_mesh[coins < 30] = 1
coins_mesh[coins > 50] = 2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs, coins_mesh, coins_stack)
assert np.all(g2[:, 0], axis=0)
assert np.all(g2[:, 1], axis=0)
num_buf = 5
# check the number of buffers are even
assert_raises(ValueError, lambda: corr.multi_tau_auto_corr(num_levels, num_buf, coins_mesh, coins_stack))
# check image shape and labels shape are equal
# assert_raises(ValueError,
# lambda : corr.multi_tau_auto_corr(num_levels, num_bufs,
# indices, coins_stack))
# check the number of pixels is zero
mesh = np.zeros_like(coins)
assert_raises(ValueError, lambda: corr.multi_tau_auto_corr(num_levels, num_bufs, mesh, coins_stack))
def test_image_stack_correlation():
num_levels = 1
num_bufs = 2 # must be even
coins = data.camera()
coins_stack = []
for i in range(4):
coins_stack.append(coins)
coins_mesh = np.zeros_like(coins)
coins_mesh[coins < 30] = 1
coins_mesh[coins > 50] = 2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs, coins_mesh,
coins_stack)
assert_almost_equal(True, np.all(g2[:, 0], axis=0))
assert_almost_equal(True, np.all(g2[:, 1], axis=0))
def test_correlation():
num_levels = 4
num_bufs = 8 # must be even
num_qs = 2 # number of interested roi's (rings)
img_dim = (50, 50) # detector size
roi_data = np.array(([10, 20, 12, 14], [40, 10, 9, 10]), dtype=np.int64)
indices = roi.rectangles(roi_data, img_dim)
img_stack = np.random.randint(1, 5, size=(500,) + img_dim)
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs, indices, img_stack)
assert_array_almost_equal(
lag_steps, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32, 40, 48, 56])
)
assert_array_almost_equal(g2[1:, 0], 1.00, decimal=2)
assert_array_almost_equal(g2[1:, 1], 1.00, decimal=2)
im = ax.imshow(image, interpolation='none', norm=LogNorm())
im = ax.imshow(tt.reshape(*img_dim), cmap='Paired',
interpolation='nearest')
roi_names = ['gray', 'orange', 'brown']
tt = np.zeros(img_stack.shape[1:]).ravel()
tt[pixel_list] = roi_indices
fig, ax = plt.subplots()
ax.set_title("NIPA_GEL_250K")
overlay_rois(ax, roi_indices, pixel_list,
img_stack.shape[1:], img_stack[0])
# g2 one time correlation results for 3 ROI's
g2, lag_steps = corr.multi_tau_auto_corr(
num_levels, num_bufs, labeled_roi_array, img_stack)
# lag_staps are delays for multiple tau analysis
lag_time = 0.001
lag_step = lag_steps[:g2.shape[0]]
lags = lag_step*lag_time
fig, axes = plt.subplots(num_rings, sharex=True, figsize=(5,10))
axes[num_rings-1].set_xlabel("lags")
for i, roi_color in zip(range(num_rings), roi_names):
axes[i].set_ylabel("g2")
axes[i].semilogx(lags, g2[:, i], 'o', markerfacecolor=roi_color, markersize=10)
axes[i].set_ylim(bottom=1, top=np.max(g2[1:, i]))
plt.show()
im = ax.imshow(image, interpolation='none', norm=LogNorm())
im = ax.imshow(tt.reshape(*img_dim),
cmap='Paired',
interpolation='nearest')
roi_names = ['gray', 'orange', 'brown']
tt = np.zeros(img_stack.shape[1:]).ravel()
tt[pixel_list] = roi_indices
fig, ax = plt.subplots()
ax.set_title("NIPA_GEL_250K")
overlay_rois(ax, roi_indices, pixel_list, img_stack.shape[1:], img_stack[0])
# g2 one time correlation results for 3 ROI's
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs,
labeled_roi_array, img_stack)
# lag_staps are delays for multiple tau analysis
lag_time = 0.001
lag_step = lag_steps[:g2.shape[0]]
lags = lag_step * lag_time
fig, axes = plt.subplots(num_rings, sharex=True, figsize=(5, 10))
axes[num_rings - 1].set_xlabel("lags")
for i, roi_color in zip(range(num_rings), roi_names):
axes[i].set_ylabel("g2")
axes[i].semilogx(lags,
g2[:, i],
'o',
markerfacecolor=roi_color,
markersize=10)
axes[i].set_ylim(bottom=1, top=np.max(g2[1:, i]))
