def check_forces(pool):
"""
Compares the forces calculated by the octtree method to
those calculated through brute force on 25 particles
"""
sample_size = 25
sim.send_data(pool)
if sample_size > sim.PARTICLE_COUNT:
sample_size = sim.PARTICLE_COUNT
indices = random.sample(range(len(sim.PARTICLES)), sample_size)
pool.imap(sim.get_forces_raw, indices)
results = []
for arr in sim.get_results(pool):
results += arr
raw_forces = sorted(results, key=lambda item: item[0])
raw_forces = np.array([i[1] for i in raw_forces])
pool.imap(sim.get_forces_tree, indices)
results = []
for arr in sim.get_results(pool):
results += arr
approx_forces = sorted(results, key=lambda item: item[0])
approx_forces = np.array([i[1] for i in approx_forces])
factors = np.abs(approx_forces / raw_forces)
factors = np.array([np.sum(i) / 3 for i in factors])
return [factors.mean(), factors.std()]
def get_timing(pool):
"""
Measures and returns the time taken to calculate the forces
on all the particles using both the particle mesh method
and the octtree
"""
sim.send_data(pool)
start_tree = time.time()
pool.imap(sim.get_forces_tree, range(sim.PARTICLE_COUNT))
sim.get_results(pool)
end_tree = time.time()
start_mesh = time.time()
sim.get_forces_mesh()
end_mesh = time.time()
return [end_tree - start_tree, end_mesh - start_mesh]
def apply_forces(pool, timestep):
"""
Calculates and applies the forces over the provided timestep
using the octtree method
"""
sim.send_data(pool)
pool.imap(sim.get_forces_tree, range(len(sim.PARTICLES)))
results = sim.get_results(pool)
for array in results:
for index, force in array:
sim.PARTICLES[index].apply_force(force, timestep)