def test_lazy_two_time():
setup()
# run the correlation on the full stack
full_gen = lazy_two_time(rois, img_stack, stack_size,
stack_size, 1)
for full_state in full_gen:
pass
final_result = two_time_state_to_results(full_state)
# make sure we have essentially zero correlation in the images,
# since they are random integers
assert np.average(final_result.g2-1) < 0.01
# run the correlation on the first half
gen_first_half = lazy_two_time(rois, img_stack[:stack_size//2], stack_size,
num_bufs=stack_size, num_levels=1)
for first_half_state in gen_first_half:
pass
# run the correlation on the second half by passing in the state from the
# first half
gen_second_half = lazy_two_time(rois, img_stack[stack_size//2:],
stack_size, num_bufs=stack_size,
num_levels=1,
two_time_internal_state=first_half_state)
for second_half_state in gen_second_half:
pass
result = two_time_state_to_results(second_half_state)
assert np.all(full_state.g2 == result.g2)
def test_lazy_two_time():
setup()
# run the correlation on the full stack
full_gen = lazy_two_time(rois, img_stack, stack_size, stack_size, 1)
for full_state in full_gen:
pass
final_result = two_time_state_to_results(full_state)
# make sure we have essentially zero correlation in the images,
# since they are random integers
assert np.average(final_result.g2 - 1) < 0.01
# run the correlation on the first half
gen_first_half = lazy_two_time(rois,
img_stack[:stack_size // 2],
stack_size,
num_bufs=stack_size,
num_levels=1)
for first_half_state in gen_first_half:
pass
# run the correlation on the second half by passing in the state from the
# first half
gen_second_half = lazy_two_time(rois,
img_stack[stack_size // 2:],
stack_size,
num_bufs=stack_size,
num_levels=1,
two_time_internal_state=first_half_state)
for second_half_state in gen_second_half:
pass
result = two_time_state_to_results(second_half_state)
assert np.all(full_state.g2 == result.g2)
def test_lazy_vs_original():
setup()
# run the correlation on the full stack
full_gen_one = lazy_one_time(
img_stack, num_levels, num_bufs, rois)
for gen_state_one in full_gen_one:
pass
g2, lag_steps = multi_tau_auto_corr(num_levels, num_bufs,
rois, img_stack)
assert np.all(g2 == gen_state_one.g2)
assert np.all(lag_steps == gen_state_one.lag_steps)
full_gen_two = lazy_two_time(rois, img_stack, stack_size,
num_bufs, num_levels)
for gen_state_two in full_gen_two:
pass
final_gen_result_two = two_time_state_to_results(gen_state_two)
two_time = two_time_corr(rois, img_stack, stack_size,
num_bufs, num_levels)
assert np.all(two_time[0] == final_gen_result_two.g2)
assert np.all(two_time[1] == final_gen_result_two.lag_steps)
def test_lazy_vs_original():
setup()
# run the correlation on the full stack
full_gen_one = lazy_one_time(
img_stack, num_levels, num_bufs, rois)
for gen_state_one in full_gen_one:
pass
g2, lag_steps = multi_tau_auto_corr(num_levels, num_bufs,
rois, img_stack)
assert np.all(g2 == gen_state_one.g2)
assert np.all(lag_steps == gen_state_one.lag_steps)
full_gen_two = lazy_two_time(rois, img_stack, stack_size,
num_bufs, num_levels)
for gen_state_two in full_gen_two:
pass
final_gen_result_two = two_time_state_to_results(gen_state_two)
two_time = two_time_corr(rois, img_stack, stack_size,
num_bufs, num_levels)
assert np.all(two_time[0] == final_gen_result_two.g2)
assert np.all(two_time[1] == final_gen_result_two.lag_steps)