def test_append_container():
particles = sparpy.Particles2()
p = sparpy.Particle2()
p.position = [1, 2]
particles.append(p)
assert particles[0].position[0] == 1
assert particles[0].position[1] == 2
def test_exponential_force():
N = 100
D = 0.001
lower_bound = [0, 0]
upper_bound = [1, 1]
periodic = [True, True]
sim_time = 10.0
number_of_observations = 100
integrate_time = sim_time / number_of_observations
dt = 0.001
epsilon = 0.01
cutoff = 0.1
particles = sparpy.Particles2(N)
for p in particles:
p.position = [
random.uniform(lower_bound[0], upper_bound[0]),
random.uniform(lower_bound[1], upper_bound[1])
]
simulation = sparpy.Simulation2()
simulation.set_domain(lower_bound, upper_bound, periodic)
simulation.add_particles(particles, D)
simulation.add_force(particles, particles,
sparpy.exponential_force2(cutoff, epsilon))
for i in range(number_of_observations):
simulation.integrate(integrate_time, dt)
assert len(particles) == N
def test_container_2d():
particles = sparpy.Particles2(10)
particles[0].position = [1, 2]
print particles[0].position
assert particles[0].position[0] == 1
assert particles[0].position[1] == 2
p = particles[1]
p.position = [3, 4]
assert particles[1].position[0] == 3
assert particles[1].position[1] == 4
particles[2] = p
assert particles[2].position[0] == 3
assert particles[2].position[1] == 4
p = sparpy.Particle2()
p.position = [5, 6]
particles[3] = p
assert particles[3].position[0] == 5
assert particles[3].position[1] == 6
particles2 = particles
particles2[0].position = [6, 7]
assert particles[0].position[0] == 6
assert particles[0].position[1] == 7
assert particles2[0].position[0] == 6
assert particles2[0].position[1] == 7
def test_exponential_force():
D = 0.001
lower_bound = [0, 0]
upper_bound = [25, 25]
periodic = [True, True]
sim_time = 10000.0
number_of_observations = 100
integrate_time = sim_time / number_of_observations
dt = 0.01
actual_time = 1
grid_dt = 100.0
grid_updates_per_observation = int(integrate_time / grid_dt)
#parameters
scale_dt = .0001
density_scale = grid_dt/dt
a = 0.2 * scale_dt
d = 0.6 * scale_dt
gamma = 0.75 * scale_dt
r = 1.0 * scale_dt
gp = 0.9 * scale_dt
gu = 0.1 * scale_dt
#lr and la cannot be zero (in the denominator)
C = {'cutoff':25, 'Ca':1, 'la':5, 'Cr':0, 'lr':0.1, 'type':0,'percent':20}
T = {'cutoff':25, 'Ca':0, 'la':0.1, 'Cr':1, 'lr':4, 'type':1, 'percent':40}
M = {'cutoff':10, 'Ca':1, 'la':5, 'Cr':0, 'lr':0.1, 'type':2, 'percent':40}
F = {'cutoff':6, 'Ca':0.05, 'la':5, 'Cr':1, 'lr':1, 'type':3, 'count': 20}
#percent_coral = 60
#percent_turf = 36
#percent_macroalgae = 4
lower_grid = 2.5
upper_grid = 22.5
step_grid = 5
grid = [lower_grid + x*step_grid for x in range(int((upper_grid-lower_grid)/step_grid)+1)]
if C['percent'] + T['percent'] + M['percent'] != 100:
print "percentages do not add up to 100"
fixed = sparpy.Particles2(len(grid) ** 2)
count = 0
for i in range(len(grid)):
for j in range(len(grid)):
fixed[count].position = [grid[i],grid[j]]
fixed[count].velocity = [0,0]
U = random.uniform(0,100)
if U <= C['percent']:
fixed[count].species = 0
elif U > C['percent'] + T['percent']:
fixed[count].species = 2
else:
fixed[count].species = 1
count = count+1
particles = sparpy.Particles2(F['count'])
for p in particles:
p.position = [random.uniform(lower_bound[0], upper_bound[0]),
random.uniform(lower_bound[1], upper_bound[1])]
p.velocity = [0,0]
p.species = F['type']
p.force = [0,0]
cutoff_exponential = 25
epsilon = 5
simulation = sparpy.Simulation2()
simulation.set_domain(lower_bound, upper_bound, periodic)
simulation.add_particles(particles, D)
simulation.add_particles(fixed, 0)
#simulation.add_force(particles, fixed, sparpy.exponential_force2(cutoff_exponential, epsilon))
simulation.add_force(particles, fixed, sparpy.morse_force2(C['cutoff'], C['Ca'], C['la'], C['Cr'], C['lr'], C['type']))
simulation.add_force(particles, fixed, sparpy.morse_force2(T['cutoff'], T['Ca'], T['la'], T['Cr'], T['lr'], T['type']))
simulation.add_force(particles, fixed, sparpy.morse_force2(M['cutoff'], M['Ca'], M['la'], M['Cr'], M['lr'], M['type']))
simulation.add_force(particles, particles, sparpy.morse_force2(F['cutoff'], F['Ca'], F['la'], F['Cr'], F['lr'], F['type']))
simulation.add_action(fixed, particles, sparpy.calculate_density2(2.5,dt/(F['count'] * grid_dt)))
simulation.add_action(fixed, fixed, sparpy.calculate_density2(step_grid,dt/grid_dt))
simulation_grid = sparpy.Simulation2()
for i in range(number_of_observations):
for j in range(grid_updates_per_observation):
for p in fixed:
p.density = [0.0,0.0,0.0,0.0]
simulation.integrate(grid_dt, dt)
for p in fixed:
U = random.uniform(0,1)
if p.species == C['type']:
if U < d * grid_dt:
p.species = T['type']
elif U < d * grid_dt + a * p.density[M['type']] * grid_dt:
p.species = M['type']
if p.species == T['type']:
if U < gamma * grid_dt * p.density[M['type']] :
p.species = M['type']
if U < (gamma * grid_dt * p.density[M['type']] +
r * grid_dt * p.density[C['type']]):
p.species = C['type']
if p.species == M['type']:
if U < (gu + gp * p.density[F['type']]) * grid_dt:
p.species = T['type']
#simulation.update_grid()
#print particles[0].force
#simulation.update_grid()
#print particles[0].force
x_av = 0
y_av = 0
for p in particles:
x_av += p.position[0]
y_av += p.position[1]
print x_av/F['count'],y_av/F['count']
assert len(particles) == F['count']