def test_exposure_time_y():
walk_data = dict()
walk_data['yinds'] = [[1, 1, 1, 1, 1]]
walk_data['xinds'] = [[1, 2, 3, 4, 5]]
walk_data['travel_times'] = [[2, 4, 6, 8, 10]]
roi = np.zeros((6, 6))
roi[2:4, :] = 1
exp_times = particle_track.exposure_time(walk_data, roi)
assert exp_times[0] == 4.0
def test_exposure_reenter():
walk_data = dict()
walk_data['yinds'] = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
walk_data['xinds'] = [[1, 2, 3, 4, 5, 4, 3, 2, 3, 4, 5]]
walk_data['travel_times'] = [[2, 4, 6, 8, 10, 11, 12, 13, 14, 15, 16]]
roi = np.zeros((6, 6))
roi[2:3, :] = 1
exp_times = particle_track.exposure_time(walk_data, roi)
assert exp_times[0] == 3.0
else:
params.diff_coeff = 1.0
# make particle
particle = pt.Particles(params)
# walk it
particle.generate_particles(Np_tracer, seed_xloc, seed_yloc)
for i in list(range(0, num_iter)):
walk_data = particle.run_iteration()
# get travel times associated with particles when they are at coord x=70
# use the exposure_time function to measure this
roi = np.zeros_like(depth, dtype='int')
roi[0:target_row, :] = 1
target_times = pt.exposure_time(walk_data, roi)
# plot histogram
plt.subplot(1, 2, dc + 1)
n, bins, _ = plt.hist(target_times,
bins=100,
range=(200, 400),
histtype='bar',
density=True,
color=[0.5, 0.5, 1, 0.5])
# plot expected travel time to row 70
plt.scatter(expected_time,
np.max(n),
s=75,
c='green',
