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Updating tabular means. Removing re-defined functions
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Patrick Shriwise
parent
9dc84fdb18
commit
85b6566fd7
1 changed files with 88 additions and 86 deletions
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@ -26,6 +26,20 @@ def test_discrete():
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assert d2.p == [1.0]
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assert len(d2) == 1
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vals = np.array([1.0, 2.0, 3.0])
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probs = np.array([0.1, 0.7, 0.2])
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exp_mean = (vals * probs).sum()
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d3 = openmc.stats.Discrete(vals, probs)
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# sample discrete distribution
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n_samples = 1_000_000
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samples = d3.sample(n_samples, seed=100)
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# check that the mean of the samples is close to the true mean
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_merge_discrete():
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x1 = [0.0, 1.0, 10.0]
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@ -65,9 +79,14 @@ def test_uniform():
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assert t.p == [1/(b-a), 1/(b-a)]
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assert t.interpolation == 'histogram'
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exp_mean = 0.5 * (a + b)
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_powerlaw():
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a, b, n = 10.0, 20.0, 2.0
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a, b, n = 10.0, 100.0, 2.0
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d = openmc.stats.PowerLaw(a, b, n)
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elem = d.to_xml_element('distribution')
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@ -77,6 +96,13 @@ def test_powerlaw():
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assert d.n == n
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assert len(d) == 3
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exp_mean = 100.0 * (n+1) / (n+2)
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# sample power law distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_maxwell():
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theta = 1.2895e6
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@ -87,6 +113,14 @@ def test_maxwell():
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assert d.theta == theta
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assert len(d) == 1
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exp_mean = 3/2 * theta
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# sample maxwell distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_watt():
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a, b = 0.965e6, 2.29e-6
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@ -98,19 +132,68 @@ def test_watt():
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assert d.b == b
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assert len(d) == 2
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d = openmc.stats.Watt(a, b)
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# mean value form adapted from
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# "Prompt-fission-neutron average energy for 238U(n, f ) from
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# threshold to 200 MeV" Ethvignot et. al.
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# https://doi.org/10.1016/j.physletb.2003.09.048
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exp_mean = 3/2 * a + a**2 * b / 4
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# sample Watt distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-03)
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def test_tabular():
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x = [0.0, 5.0, 7.0]
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p = [0.1, 0.2, 0.05]
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x = np.array([0.0, 5.0, 7.0])
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p = np.array([0.1, 0.2, 0.05])
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d = openmc.stats.Tabular(x, p, 'linear-linear')
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elem = d.to_xml_element('distribution')
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d = openmc.stats.Tabular.from_xml_element(elem)
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assert d.x == x
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assert d.p == p
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assert all(d.x == x)
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assert all(d.p == p)
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assert d.interpolation == 'linear-linear'
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assert len(d) == len(x)
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# test linear-lienar sampling
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d = openmc.stats.Tabular(x, p)
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# compute the expected value (mean) of the
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# piecewise pdf
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mean = 0.0
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d.normalize()
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for i in range(1, len(x)):
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y_min = d.p[i-1]
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y_max = d.p[i]
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x_min = d.x[i-1]
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x_max = d.x[i]
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m = (y_max - y_min) / (x_max - x_min)
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exp_val = (1./3.) * m * (x_max**3 - x_min**3)
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exp_val += 0.5 * m * x_min * (x_min**2 - x_max**2)
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exp_val += 0.5 * y_min * (x_max**2 - x_min**2)
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mean += exp_val
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n_samples = 100_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(mean, rel=1e-03)
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# test histogram sampling
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d = openmc.stats.Tabular(x, p, interpolation='histogram')
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d.normalize()
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mean = 0.5 * (x[:-1] + x[1:])
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mean *= np.diff(d.cdf())
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mean = sum(mean)
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(mean, rel=1e-03)
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def test_legendre():
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# Pu239 elastic scattering at 100 keV
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@ -254,87 +337,6 @@ def test_point():
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assert d.xyz == pytest.approx(p)
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def test_discrete():
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vals = np.array([1.0, 2.0, 3.0])
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probs = np.array([0.1, 0.7, 0.2])
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exp_mean = (vals * probs).sum()
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d = openmc.stats.Discrete(vals, probs)
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# sample discrete distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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# check that the mean of the samples is close to the true mean
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_uniform():
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lower = 1.1
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upper = 23.3
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exp_mean = 0.5 * (lower + upper)
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d = openmc.stats.Uniform(lower, upper)
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# sample the uniform distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_power_law():
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lower = 0.0
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upper = 1.0
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exponent = 2.0
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d = openmc.stats.PowerLaw(lower, upper, exponent)
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exp_mean = (exponent + 1) / (exponent + 2)
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# sample power law distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_maxwell():
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theta = 1000
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exp_mean = 0
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d = openmc.stats.Maxwell(theta)
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exp_mean = 3/2 * theta
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# sample maxwell distribution
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n_samples = 1_000_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_watt():
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a = 10
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b = 20
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d = openmc.stats.Watt(a, b)
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# mean value form adapted from
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# "Prompt-fission-neutron average energy for 238U(n, f ) from
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# threshold to 200 MeV" Ethvignot et. al.
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# https://doi.org/10.1016/j.physletb.2003.09.048
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exp_mean = 3/2 * a + a**2 * b / 4
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# sample Watt distribution
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n_samples = 100_000
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samples = d.sample(n_samples, seed=100)
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assert samples.mean() == pytest.approx(exp_mean, rel=1e-02)
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def test_normal():
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mean = 10.0
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std_dev = 2.0
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