def test_moment_0d():
# Arrange
n_part = 10000
v_mean = 2e-6
d = 1.2
v_min = 0.01e-6
v_max = 10e-6
n_sd = 32
spectrum = Lognormal(n_part, v_mean, d)
v, n = linear(n_sd, spectrum, (v_min, v_max))
T = np.full_like(v, 300.)
n = discretise_n(n)
particles = DummyCore(backend, n_sd)
attribute = {'n': n, 'volume': v, 'temperature': T}
particles.build(attribute)
state = particles.state
true_mean, true_var = spectrum.stats(moments='mv')
# TODO: add a moments_0 wrapper
moment_0 = particles.backend.Storage.empty((1,), dtype=int)
moments = particles.backend.Storage.empty((1, 1), dtype=float)
# Act
state.moments(moment_0, moments, specs={'volume': (0,)})
discr_zero = moments[0, 0]
state.moments(moment_0, moments, specs={'volume': (1,)})
discr_mean = moments[0, 0]
state.moments(moment_0, moments, specs={'volume': (2,)})
discr_mean_radius_squared = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (0,)})
discr_zero_T = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (1,)})
discr_mean_T = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (2,)})
discr_mean_T_squared = moments[0, 0]
# Assert
assert abs(discr_zero - 1) / 1 < 1e-3
assert abs(discr_mean - true_mean) / true_mean < .01e-1
true_mrsq = true_var + true_mean**2
assert abs(discr_mean_radius_squared - true_mrsq) / true_mrsq < .05e-1
assert discr_zero_T == discr_zero
assert discr_mean_T == 300.
assert discr_mean_T_squared == 300. ** 2
def test_moment_0d(backend):
# Arrange
n_part = 100000
v_mean = 2e-6
d = 1.2
n_sd = 32
spectrum = Lognormal(n_part, v_mean, d)
v, n = Linear(spectrum).sample(n_sd)
T = np.full_like(v, 300.)
n = discretise_n(n)
particles = DummyCore(backend, n_sd)
attribute = {'n': n, 'volume': v, 'temperature': T, 'heat': T * v}
particles.build(attribute)
state = particles.particles
true_mean, true_var = spectrum.stats(moments='mv')
# TODO #217 : add a moments_0 wrapper
moment_0 = particles.backend.Storage.empty((1, ), dtype=int)
moments = particles.backend.Storage.empty((1, 1), dtype=float)
# Act
state.moments(moment_0, moments, specs={'volume': (0, )})
discr_zero = moments[0, 0]
state.moments(moment_0, moments, specs={'volume': (1, )})
discr_mean = moments[0, 0]
state.moments(moment_0, moments, specs={'volume': (2, )})
discr_mean_radius_squared = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (0, )})
discr_zero_T = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (1, )})
discr_mean_T = moments[0, 0]
state.moments(moment_0, moments, specs={'temperature': (2, )})
discr_mean_T_squared = moments[0, 0]
# Assert
assert abs(discr_zero - 1) / 1 < 1e-3
assert abs(discr_mean - true_mean) / true_mean < .01e-1
true_mrsq = true_var + true_mean**2
assert abs(discr_mean_radius_squared - true_mrsq) / true_mrsq < .05e-1
assert discr_zero_T == discr_zero
assert discr_mean_T == 300.
np.testing.assert_approx_equal(discr_mean_T_squared,
300.**2,
significant=6)
