epsilon = nb.constants.AU#/nb.constants.PC #Smoothing parameter
d = 1.0e14 #/nb.constants.PC
alpha = 0.0001 #adaptive time step parameter
N = 500
bodies, distribution = nb.icc.plummer(N, d)
def mass_colors(snapshots, time_step):
curr_masses = snapshots[time_step, :, 6]
norm_mass = (curr_masses - curr_masses.min()) / (curr_masses.max() - curr_masses.min())
cmap = cm.get_cmap("RdBu")
return [cmap(abs(1.0 - norm_mass[i])) for i in range(norm_mass.shape[0])]
snapshot_storage = SnapshotStorage()
snapshot_storage.append(bodies)
snapshot_renderer = SnapshotRenderer.for_clusters(snapshot_storage, recoloring_func=mass_colors, bounds=[-2e14, 2e14], verbose=1, angle=[45, 45])
#snapshot_renderer.display_step()
for i, current_t in enumerate(nb.leapfrog_adaptive.simulate_step(bodies, G=nb.constants.G, epsilon=epsilon, dt_output=dt_output, alpha=alpha, max_dt_bins=5)):
if current_t >= total_time:
break
print "{}/{}".format(current_t/nb.constants.YR, total_time/nb.constants.YR)
snapshot_storage.append(bodies)
#snapshot_renderer.display_step()
#nb.constants.G, nb.constants.SOLAR_MASS, nb.constants.YR = nb.constants.codetounits() #Back to SI units
[1 * nb.constants.AU, 0.0, 0.0]])
bodies.m = np.array([nb.constants.SOLAR_MASS, 3.3e23, 4.86e24, 5.97e24])
bodies.v = np.array([
[0.0, 0.0, 0.0],
[0.0, 47362, 0.0],
[0.0, -35020, 0.0],
[0.0, 29780, 0.0]])
total_time = 10 * nb.constants.YR
dt_min = 0.000001 * nb.constants.YR
epsilon = 0.2
alpha = 0.001 #adaptive time step parameter
dt_output = 0.01 * nb.constants.YR
snapshot_storage = SnapshotStorage()
snapshot_storage.append(bodies.r)
snapshot_renderer = SnapshotRenderer.for_orbits(snapshot_storage,
bounds=(-nb.constants.AU, nb.constants.AU))
snapshot_renderer.display_step()
for i, current_t in enumerate(leapfrog_adaptive.simulate_step(bodies, dt_min, G=nb.constants.G, epsilon=epsilon, dt_output=dt_output, alpha=alpha)):
if current_t >= total_time:
break
print "{}/{}".format(current_t, total_time)
snapshot_storage.append(bodies.r)
snapshot_renderer.display_step()
[0.0, -35020, 0.0],
[0.0, 29780, 0.0]])
total_time = 10 * nb.constants.YR
dt_min = 0.000001 * nb.constants.YR
epsilon = 0.2
alpha = 0.001 #adaptive time step parameter
dt_output = 1.0 * nb.constants.YR
bodies_phys = bodies.clone()
conversion_params = nb.constants.convert_to_sim_units(bodies)
space_coeff = nb.constants.space_coeff(*conversion_params)
time_coeff = nb.constants.time_coeff(*conversion_params)
snapshot_storage = SnapshotStorage()
snapshot_storage.append(bodies_phys)
snapshot_renderer = SnapshotRenderer.for_orbits(snapshot_storage,
bounds=(-nb.constants.AU, nb.constants.AU))
snapshot_renderer.display_step()
for i, current_t in enumerate(nb.leapfrog_adaptive.simulate_step(bodies,
epsilon=epsilon / space_coeff, dt_output=dt_output / space_coeff, alpha=alpha, max_dt_bins=5)):
if current_t >= total_time / time_coeff:
break
print "{}/{}".format(current_t * time_coeff / nb.constants.YR, total_time / nb.constants.YR)
bodies_phys = bodies.clone()
nb.constants.convert_from_sim_units(bodies_phys, *conversion_params)
bodies.v = np.array([[0.0, 0.0, 0.0], [0.0, 47362, 0.0], [0.0, -35020, 0.0],
[0.0, 29780, 0.0]])
total_time = 10 * nb.constants.YR
dt_min = 0.000001 * nb.constants.YR
epsilon = 0.2
alpha = 0.001 #adaptive time step parameter
dt_output = 1.0 * nb.constants.YR
bodies_phys = bodies.clone()
conversion_params = nb.constants.convert_to_sim_units(bodies)
space_coeff = nb.constants.space_coeff(*conversion_params)
time_coeff = nb.constants.time_coeff(*conversion_params)
snapshot_storage = SnapshotStorage()
snapshot_storage.append(bodies_phys)
snapshot_renderer = SnapshotRenderer.for_orbits(snapshot_storage,
bounds=(-nb.constants.AU,
nb.constants.AU))
snapshot_renderer.display_step()
for i, current_t in enumerate(
nb.leapfrog_adaptive.simulate_step(bodies,
epsilon=epsilon / space_coeff,
dt_output=dt_output / space_coeff,
alpha=alpha,
max_dt_bins=5)):
if current_t >= total_time / time_coeff:
break
bodies, distribution = nb.icc.plummer(N, d)
bodies_phys = bodies.clone()
conversion_params = nb.constants.convert_to_sim_units(bodies)
def mass_colors(snapshots, time_step):
curr_masses = snapshots[time_step, :, 6]
norm_mass = (curr_masses - curr_masses.min()) / (curr_masses.max() -
curr_masses.min())
cmap = cm.get_cmap("RdBu")
return [cmap(abs(1.0 - norm_mass[i])) for i in range(norm_mass.shape[0])]
snapshot_storage = SnapshotStorage()
snapshot_storage.append(bodies_phys)
space_coeff = nb.constants.space_coeff(*conversion_params)
snapshot_renderer = SnapshotRenderer.for_clusters(snapshot_storage,
recoloring_func=mass_colors,
bounds=[-2e14, 2e14],
verbose=1,
angle=[45, 45])
#snapshot_renderer.display_step()
time_coeff = nb.constants.time_coeff(*conversion_params)
for i, current_t in enumerate(
nb.leapfrog_adaptive.simulate_step(bodies,
epsilon=epsilon / space_coeff,
alpha=alpha,