def test_multi_collision(self, v_2, n_2, p):
# Arrange
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(
len(n_2))
sut.compute_gamma = lambda prob, rand: backend_fill(backend, prob, p)
particles.state = TestableStateFactory.state_0d(
n=n_2,
extensive={'volume': v_2},
intensive={},
particles=particles)
# Act
sut()
# Assert
state = particles.state
gamma = min(p, max(n_2[0] // n_2[1], n_2[1] // n_2[1]))
assert np.amin(state['n']) >= 0
assert np.sum(state['n'] * state['volume']) == np.sum(n_2 * v_2)
assert np.sum(state['n']) == np.sum(n_2) - gamma * np.amin(n_2)
assert np.amax(
state['volume']
) == gamma * v_2[np.argmax(n_2)] + v_2[np.argmax(n_2) - 1]
assert np.amax(state['n']) == max(
np.amax(n_2) - gamma * np.amin(n_2), np.amin(n_2))
def test_single_collision(self, v_2, T_2, n_2):
# Arrange
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(
len(n_2))
sut.compute_gamma = lambda prob, rand: backend_fill(backend, prob, 1)
particles.state = TestableStateFactory.state_0d(
n=n_2,
extensive={'volume': v_2},
intensive={'temperature': T_2},
particles=particles)
# Act
sut()
# Assert
state = particles.state
assert np.sum(state['n'] * state['volume'] *
state['temperature']) == np.sum(n_2 * T_2 * v_2)
new_T = np.sum(T_2 * v_2) / np.sum(v_2)
assert np.isin(round(new_T, 10), np.round(state['temperature'], 10))
assert np.sum(particles.state['n'] *
particles.state['volume']) == np.sum(n_2 * v_2)
assert np.sum(particles.state['n']) == np.sum(n_2) - np.amin(n_2)
if np.amin(n_2) > 0:
assert np.amax(particles.state['volume']) == np.sum(v_2)
assert np.amax(particles.state['n']) == max(
np.amax(n_2) - np.amin(n_2), np.amin(n_2))
def test_multi_droplet(self, x, n, p):
# Arrange
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(len(n))
sut.compute_gamma = lambda prob, rand: backend_fill(
backend, prob, p, True)
particles.state = TestableStateFactory.state_0d(
n=n, extensive={'volume': x}, intensive={}, particles=particles)
# Act
sut()
# Assert
assert np.amin(particles.state['n']) >= 0
assert np.sum(particles.state['n'] *
particles.state['volume']) == np.sum(n * x)
def test_single_collision_same_n(self, n_in, n_out):
# Arrange
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(2)
sut.compute_gamma = lambda prob, rand: backend_fill(backend, prob, 1)
particles.state = TestableStateFactory.state_0d(
n=np.full(2, n_in),
extensive={'volume': np.full(2, 1.)},
intensive={},
particles=particles)
# Act
sut()
# Assert
np.testing.assert_array_equal(
sorted(particles.backend.to_ndarray(particles.state.n)),
sorted(n_out))
def test_single_collision(self, x_2, n_2):
# Arrange
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(
len(n_2))
sut.compute_gamma = lambda prob, rand: backend_fill(backend, prob, 1)
particles.state = TestableStateFactory.state_0d(
n=n_2,
extensive={'volume': x_2},
intensive={},
particles=particles)
# Act
sut()
# Assert
assert np.sum(particles.state['n'] *
particles.state['volume']) == np.sum(n_2 * x_2)
assert np.sum(particles.state['n']) == np.sum(n_2) - np.amin(n_2)
if np.amin(n_2) > 0:
assert np.amax(particles.state['volume']) == np.sum(x_2)
assert np.amax(particles.state['n']) == max(
np.amax(n_2) - np.amin(n_2), np.amin(n_2))
def test_multi_step(self):
# Arrange
n_sd = 256
n = np.random.randint(1, 64, size=n_sd)
x = np.random.uniform(size=n_sd)
particles, sut = TestSDMSingleCell.get_dummy_particles_and_sdm(n_sd)
sut.compute_gamma = lambda prob, rand: backend_fill(
backend, prob, backend.to_ndarray(rand) > 0.5, odd_zeros=True)
particles.state = TestableStateFactory.state_0d(
n=n, extensive={'volume': x}, intensive={}, particles=particles)
# Act
for _ in range(32):
sut()
# Assert
assert np.amin(particles.state['n']) >= 0
actual = np.sum(particles.state['n'] * particles.state['volume'])
desired = np.sum(n * x)
np.testing.assert_almost_equal(actual=actual, desired=desired)