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=float)
moments = particles.backend.Storage.empty((1, 1), dtype=float)
# Act
state.moments(moment_0, moments, specs={'volume': (0, )})
discr_zero = moments[0, slice(0, 1)].to_ndarray()
state.moments(moment_0, moments, specs={'volume': (1, )})
discr_mean = moments[0, slice(0, 1)].to_ndarray()
state.moments(moment_0, moments, specs={'volume': (2, )})
discr_mean_radius_squared = moments[0, slice(0, 1)].to_ndarray()
state.moments(moment_0, moments, specs={'temperature': (0, )})
discr_zero_T = moments[0, slice(0, 1)].to_ndarray()
state.moments(moment_0, moments, specs={'temperature': (1, )})
discr_mean_T = moments[0, slice(0, 1)].to_ndarray()
state.moments(moment_0, moments, specs={'temperature': (2, )})
discr_mean_T_squared = moments[0, slice(0, 1)].to_ndarray()
# 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)
def test_spectrum_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
v_bins = np.linspace(0, 5e-6, num=5, endpoint=True)
true_mean, true_var = spectrum.stats(moments='mv')
# TODO #217 : add a moments_0 wrapper
spectrum_moment_0 = particles.backend.Storage.empty(
(len(v_bins) - 1, 1), dtype=float)
spectrum_moments = particles.backend.Storage.empty(
(len(v_bins) - 1, 1), dtype=float)
moment_0 = particles.backend.Storage.empty((1, ), dtype=float)
moments = particles.backend.Storage.empty((1, 1), dtype=float)
v_bins_edges = particles.backend.Storage.from_ndarray(v_bins)
# Act
state.spectrum_moments(spectrum_moment_0,
spectrum_moments,
attr='volume',
rank=1,
attr_bins=v_bins_edges)
actual = spectrum_moments.to_ndarray()
expected = np.empty((len(v_bins) - 1, 1), dtype=float)
for i in range(len(v_bins) - 1):
state.moments(moment_0,
moments,
specs={'volume': (1, )},
attr_range=(v_bins[i], v_bins[i + 1]))
expected[i, 0] = moments[0, 0]
# Assert
np.testing.assert_array_almost_equal(actual, expected)