def test_Particles():
rs = np.random.RandomState(_SEED)
box = pbm.Box(x1=-4.e-6, x2=4.e-6, y1=-4.e-6, y2=4.e-6, z1=-6e-6, z2=6e-6)
D1 = 12e-12
D2 = D1 / 2
P = pbm.Particles(num_particles=20, D=D1, box=box, rs=rs)
P.add(num_particles=15, D=D2)
Di, counts = list(zip(*P.diffusion_coeff_counts))
rs2 = np.random.RandomState()
rs2.set_state(P.init_random_state)
P2_list = pbm.Particles._generate(num_particles=counts[0],
D=Di[0],
box=P.box,
rs=rs2)
P2_list += pbm.Particles._generate(num_particles=counts[1],
D=Di[1],
box=P.box,
rs=rs2)
assert P.to_list() == P2_list
# Test Particles random states
assert randomstate_equal(P.rs, rs.get_state())
assert randomstate_equal(P.init_random_state, np.random.RandomState(_SEED))
assert not randomstate_equal(P.init_random_state, P.rs)
# Test JSON serialization
P_json = P.to_json()
P3 = pbm.Particles.from_json(P_json)
assert P.to_list() == P3.to_list()
def test_diffusion_sim_random_state():
# Initialize the random state
rs = np.random.RandomState(_SEED)
# Diffusion coefficient
Du = 12.0 # um^2 / s
D1 = Du * (1e-6)**2 # m^2 / s
D2 = D1 / 2
# Simulation box definition
box = pbm.Box(x1=-4.e-6, x2=4.e-6, y1=-4.e-6, y2=4.e-6, z1=-6e-6, z2=6e-6)
# PSF definition
psf = pbm.NumericPSF()
# Particles definition
P = pbm.Particles(num_particles=20, D=D1, box=box, rs=rs)
P.add(num_particles=15, D=D2)
# Simulation time step (seconds)
t_step = 0.5e-6
# Time duration of the simulation (seconds)
t_max = 0.01
# Particle simulation definition
S = pbm.ParticlesSimulation(t_step=t_step,
t_max=t_max,
particles=P,
box=box,
psf=psf)
rs_prediffusion = rs.get_state()
S.simulate_diffusion(total_emission=False,
save_pos=True,
verbose=True,
rs=rs,
chunksize=2**13,
chunkslice='times')
rs_postdiffusion = rs.get_state()
# Test diffusion random states
saved_rs = S.traj_group._v_attrs['init_random_state']
assert randomstate_equal(saved_rs, rs_prediffusion)
saved_rs = S.traj_group._v_attrs['last_random_state']
assert randomstate_equal(saved_rs, rs_postdiffusion)
def test_Particles():
rs = np.random.RandomState(_SEED)
P = pbm.Particles(num_particles=20, D=D1, box=box, rs=rs)
P.add(num_particles=15, D=D2)
assert P.particles_counts == [20, 15]
assert P.num_populations == 2
assert P.diffusion_coeff_counts == [(D1, 20), (D2, 15)]
with pytest.raises(ValueError):
P.add(num_particles=1, D=D1)
x1 = P.num_particles_to_slices((7, 8))
x2 = [slice(0, 7, None), slice(7, 7 + 8, None)]
for s1, s2 in zip(x1, x2):
assert s1 == s2
Di, counts = zip(*P.diffusion_coeff_counts)
rs2 = np.random.RandomState()
rs2.set_state(P.init_random_state)
P2_list = pbm.Particles._generate(num_particles=counts[0],
D=Di[0],
box=P.box,
rs=rs2)
P2_list += pbm.Particles._generate(num_particles=counts[1],
D=Di[1],
box=P.box,
rs=rs2)
assert P.to_list() == P2_list
# Test Particles random states
assert randomstate_equal(P.rs, rs.get_state())
assert randomstate_equal(P.init_random_state, np.random.RandomState(_SEED))
assert not randomstate_equal(P.init_random_state, P.rs)
# Test JSON serialization
P_json = P.to_json()
P3 = pbm.Particles.from_json(P_json)
assert P.to_list() == P3.to_list()
# Test alternative constructor
rs = np.random.RandomState(_SEED)
P4 = pbm.Particles.from_specs(num_particles=(20, 15),
D=(D1, D2),
box=box,
rs=rs)
assert P4.to_list() == P2_list
def create_diffusion_sim():
rs = np.random.RandomState(_SEED)
Du = 12.0 # um^2 / s
D = Du * (1e-6)**2 # m^2 / s
box = pbm.Box(x1=-4.e-6, x2=4.e-6, y1=-4.e-6, y2=4.e-6, z1=-6e-6, z2=6e-6)
psf = pbm.NumericPSF()
P = pbm.Particles(num_particles=100, D=D, box=box, rs=rs)
t_step = 0.5e-6
t_max = 0.1
S = pbm.ParticlesSimulation(t_step=t_step,
t_max=t_max,
particles=P,
box=box,
psf=psf)
S.simulate_diffusion(save_pos=True,
total_emission=False,
radial=True,
rs=rs)
S.store.close()
return S.hash()[:6]
def test_diffusion_sim_core():
# Initialize the random state
rs = np.random.RandomState(_SEED)
Du = 12.0 # um^2 / s
D = Du * (1e-6)**2 # m^2 / s
box = pbm.Box(x1=-4.e-6, x2=4.e-6, y1=-4.e-6, y2=4.e-6, z1=-6e-6, z2=6e-6)
psf = pbm.NumericPSF()
P = pbm.Particles(num_particles=100, D=D, box=box, rs=rs)
t_step = 0.5e-6
t_max = 0.001
time_size = t_max / t_step
assert t_max < 1e4
S = pbm.ParticlesSimulation(t_step=t_step,
t_max=t_max,
particles=P,
box=box,
psf=psf)
start_pos = [p.r0 for p in S.particles]
start_pos = np.vstack(start_pos).reshape(S.num_particles, 3, 1)
for wrap_func in [pbm.diffusion.wrap_mirror, pbm.diffusion.wrap_periodic]:
for total_emission in [True, False]:
sim = S._sim_trajectories(time_size,
start_pos,
rs=rs,
total_emission=total_emission,
save_pos=True,
wrap_func=wrap_func)
POS, em = sim
POS = np.concatenate(POS, axis=0)
#x, y, z = POS[:, :, 0], POS[:, :, 1], POS[:, :, 2]
#r_squared = x**2 + y**2 + z**2
DR = np.diff(POS, axis=2)
dx, dy, dz = DR[:, :, 0], DR[:, :, 1], DR[:, :, 2]
dr_squared = dx**2 + dy**2 + dz**2
D_fitted = dr_squared.mean() / (6 * t_max) # Fitted diffusion coefficient
assert np.abs(D - D_fitted) < 0.01