def test_variance1(start_loc, time_step):
"""
After a few timesteps the variance of the particle positions should be
similar to the computed value: var = Dt
"""
num_le = 1000
start_time = datetime.datetime(2012, 11, 10, 0)
sc = sample_sc_release(num_le, start_loc, start_time)
D = 100000
num_steps = 10
rand = RandomMover(diffusion_coef=D)
model_time = start_time
for i in range(num_steps):
model_time += datetime.timedelta(seconds=time_step)
sc.release_elements(time_step, model_time)
rand.prepare_for_model_step(sc, time_step, model_time)
delta = rand.get_move(sc, time_step, model_time)
# print "delta:", delta
sc['positions'] += delta
# print sc['positions']
# compute the variances:
# convert to meters
pos = FlatEarthProjection.lonlat_to_meters(sc['positions'],
start_loc)
var = np.var(pos, axis=0)
# D converted to meters^s/s
expected = 2.0 * (D * 1e-4) * num_steps * time_step
assert np.allclose(var, (expected, expected, 0.), rtol=0.1)
def test_variance1(start_loc, time_step):
"""
After a few timesteps the variance of the particle positions should be
similar to the computed value: var = Dt
"""
num_le = 1000
start_time = datetime.datetime(2012, 11, 10, 0)
sc = sample_sc_release(num_le, start_loc, start_time)
D = 100000
num_steps = 10
rand = RandomMover(diffusion_coef=D)
model_time = start_time
for i in range(num_steps):
model_time += datetime.timedelta(seconds=time_step)
sc.release_elements(time_step, model_time)
rand.prepare_for_model_step(sc, time_step, model_time)
delta = rand.get_move(sc, time_step, model_time)
# print "delta:", delta
sc['positions'] += delta
# print sc['positions']
# compute the variances:
# convert to meters
pos = FlatEarthProjection.lonlat_to_meters(sc['positions'], start_loc)
var = np.var(pos, axis=0)
# D converted to meters^s/s
expected = 2.0 * (D * 1e-4) * num_steps * time_step
assert np.allclose(var, (expected, expected, 0.), rtol=0.1)
