def test_NumericPSF_hash_roundtrip():
psf = pbm.NumericPSF()
with tables.open_file('psfntest.h5', mode='w') as h5:
psf.to_hdf5(h5)
with tables.open_file('psfntest.h5', mode='r') as h5:
a = h5.get_node('/xz_realistic_z50_150_160_580nm_n1335_HR2')
assert pbm.NumericPSF(psf_pytables=a).hash() == psf.hash()
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 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 create_diffusion_sim(psf=pbm.NumericPSF()):
rs = np.random.RandomState(_SEED)
specs = {
k: v
for k, v in particles_specs.items()
if k in ['num_particles', 'D', 'box']
}
P = pbm.Particles.from_specs(**specs, rs=rs)
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=False,
rs=rs)
S.store.close()
return S.hash()[:6]
def test_TimestampSimulation():
for psf in (pbm.GaussianPSF(), pbm.NumericPSF()):
hash_ = create_diffusion_sim(psf)
S = pbm.ParticlesSimulation.from_datafile(hash_, mode='a')
params = dict(
em_rates=(400e3,
), # Peak emission rates (cps) for each population (D+A)
E_values=(0.75, ), # FRET efficiency for each population
num_particles=(1, ), # Number of particles in each population
bg_rate_d=1400, # Poisson background rate (cps) Donor channel
bg_rate_a=800, # Poisson background rate (cps) Acceptor channel
)
mix_sim = pbm.TimestampSimulation(S, **params)
mix_sim.summarize()
rs = np.random.RandomState(_SEED)
mix_sim.run(rs=rs, overwrite=True)
mix_sim.save_photon_hdf5()
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
def test_diffusion_sim_random_state():
for psf in (pbm.NumericPSF(), pbm.GaussianPSF()):
# Initialize the random state
rs = np.random.RandomState(_SEED)
# Particles definition
P = pbm.Particles.from_specs(num_particles=(5, 7),
D=(D1, D2),
box=box,
rs=rs)
# 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)
S.store.close()
def test_diffusion_sim_core_npsf():
_test_diffusion_sim_core(pbm.NumericPSF())
# 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)
# Particles definition
n1 = 10
n2 = 0
#nn = n1+n2
P = pbm.Particles.from_specs(num_particles=(n1,)
,D=(D1,)
,box=box
,rs=rs)
# PSF definition
#psf = pbm.NumericPSF()
#psf = pbm.GaussianPSF(sx=0.3e-6,sy=0.3e-6,sz=0.5e-6)
psf = pbm.NumericPSF()
# Particle simulation definition
S = pbm.ParticlesSimulation(t_step=t_step, t_max=t_max,
particles=P, box=box, psf=psf)
###Run simulation:
#Note that the save_pos=True seems to be very important in order to use the
#particle simulation visualizer (PSV)
print('Current random state:', pbm.hashfunc(rs.get_state()))
S.simulate_diffusion(total_emission=False, save_pos=True, verbose=True,
rs=rs, chunksize=2**19, chunkslice='times')
print('Current random state:', pbm.hashfunc(rs.get_state()))
print(S.compact_name())
#%% Simulate FRET:
