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1148 lines
39 KiB
Python
1148 lines
39 KiB
Python
from math import pi
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from pathlib import Path
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import numpy as np
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import pytest
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import openmc
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import openmc.stats
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from openmc.stats.univariate import _INTERPOLATION_SCHEMES, DecaySpectrum
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from scipy.integrate import trapezoid
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from tests.unit_tests import assert_sample_mean
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@pytest.mark.flaky(reruns=1)
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def test_discrete():
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x = [0.0, 1.0, 10.0]
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p = [0.3, 0.2, 0.5]
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d = openmc.stats.Discrete(x, p)
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elem = d.to_xml_element('distribution')
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d = openmc.stats.Discrete.from_xml_element(elem)
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np.testing.assert_array_equal(d.x, x)
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np.testing.assert_array_equal(d.p, p)
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assert len(d) == len(x)
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d = openmc.stats.Univariate.from_xml_element(elem)
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assert isinstance(d, openmc.stats.Discrete)
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# Single point
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d2 = openmc.stats.Discrete(1e6, 1.0)
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assert d2.x == [1e6]
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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 and check that the mean of the samples is
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# within 4 std. dev. of the expected mean
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n_samples = 1_000_000
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samples, weights = d3.sample(n_samples)
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assert_sample_mean(samples, exp_mean)
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assert np.all(weights == 1.0)
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# Test biased distribution
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d3.bias = np.array([0.2, 0.1, 0.7])
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bias_elem = d3.to_xml_element('distribution')
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d4 = openmc.stats.Univariate.from_xml_element(bias_elem)
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np.testing.assert_array_equal(d4.bias, [0.2, 0.1, 0.7])
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samples, weights = d4.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, exp_mean)
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assert np.all(weights != 1.0)
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def test_delta_function():
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d = openmc.stats.delta_function(14.1e6)
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assert isinstance(d, openmc.stats.Discrete)
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np.testing.assert_array_equal(d.x, [14.1e6])
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np.testing.assert_array_equal(d.p, [1.0])
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def test_merge_discrete():
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x1 = [0.0, 1.0, 10.0]
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p1 = [0.3, 0.2, 0.5]
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d1 = openmc.stats.Discrete(x1, p1)
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x2 = [0.5, 1.0, 5.0]
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p2 = [0.4, 0.5, 0.1]
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d2 = openmc.stats.Discrete(x2, p2)
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# Merged distribution should have x values sorted and probabilities
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# appropriately combined. Duplicate x values should appear once.
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merged = openmc.stats.Discrete.merge([d1, d2], [0.6, 0.4])
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assert merged.x == pytest.approx([0.0, 0.5, 1.0, 5.0, 10.0])
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assert merged.p == pytest.approx(
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[0.6*0.3, 0.4*0.4, 0.6*0.2 + 0.4*0.5, 0.4*0.1, 0.6*0.5])
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assert merged.integral() == pytest.approx(1.0)
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# Probabilities add up but are not normalized
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d1 = openmc.stats.Discrete([3.0], [1.0])
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triple = openmc.stats.Discrete.merge([d1, d1, d1], [1.0, 2.0, 3.0])
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assert triple.x == pytest.approx([3.0])
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assert triple.p == pytest.approx([6.0])
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assert triple.integral() == pytest.approx(6.0)
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def test_merge_discrete_with_bias():
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# Two discrete distributions with different biases
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d1 = openmc.stats.Discrete([1.0, 2.0], [0.5, 0.5])
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d2 = openmc.stats.Discrete([2.0, 3.0], [0.3, 0.7], bias=[0.1, 0.9])
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merged = openmc.stats.Discrete.merge([d1, d2], [0.6, 0.4])
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exp_mean = 0.6 * d1.mean() + 0.4 * d2.mean()
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# Verify merged distribution has correct x values
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assert set(merged.x) == {1.0, 2.0, 3.0}
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# Bias should not be changed in original distributions
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assert d1.bias is None
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assert np.all(d2.bias == [0.1, 0.9])
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# Sample and verify bias is applied correctly
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samples, weights = merged.sample(10_000)
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# Verify weighted mean matches expected unbiased mean
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assert_sample_mean(samples*weights, exp_mean)
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def test_clip_discrete():
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# Create discrete distribution with two points that are not important, one
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# because the x value is very small, and one because the p value is very
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# small
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d = openmc.stats.Discrete([1e-8, 1.0, 2.0, 1000.0], [3.0, 2.0, 5.0, 1e-12])
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# Clipping the distribution should result in two points
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d_clip = d.clip(1e-6)
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assert d_clip.x.size == 2
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assert d_clip.p.size == 2
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# Make sure inplace returns same object
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d_same = d.clip(1e-6, inplace=True)
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assert d_same is d
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with pytest.raises(ValueError):
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d.clip(-1.)
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with pytest.raises(ValueError):
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d.clip(5)
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@pytest.mark.flaky(reruns=1)
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def test_uniform():
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a, b = 10.0, 20.0
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d = openmc.stats.Uniform(a, b)
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elem = d.to_xml_element('distribution')
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d = openmc.stats.Uniform.from_xml_element(elem)
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assert d.a == a
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assert d.b == b
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assert len(d) == 2
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t = d.to_tabular()
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np.testing.assert_array_equal(t.x, [a, b])
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np.testing.assert_array_equal(t.p, [1/(b-a), 1/(b-a)])
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assert t.interpolation == 'histogram'
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# Sample distribution and check that the mean of the samples is within 4
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# std. dev. of the expected mean
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exp_mean = 0.5 * (a + b)
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n_samples = 1_000_000
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, exp_mean)
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assert np.all(weights == 1.0)
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# Test biased distribution
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d.bias = openmc.stats.PowerLaw(a, b, 2)
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bias_elem = d.to_xml_element('distribution')
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d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d2.bias, openmc.stats.PowerLaw)
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samples, weights = d2.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, exp_mean)
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assert np.all(weights != 1.0)
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@pytest.mark.flaky(reruns=1)
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def test_powerlaw():
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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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d = openmc.stats.PowerLaw.from_xml_element(elem)
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assert d.a == a
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assert d.b == b
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assert d.n == n
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assert len(d) == 3
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# Determine mean of distribution
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exp_mean = (n+1)*(b**(n+2) - a**(n+2))/((n+2)*(b**(n+1) - a**(n+1)))
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# sample power law distribution and check that the mean of the samples is
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# within 4 std. dev. of the expected mean
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n_samples = 1_000_000
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, exp_mean)
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assert np.all(weights == 1.0)
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# Test biased distribution
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d.bias = openmc.stats.Uniform(a, b)
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bias_elem = d.to_xml_element('distribution')
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d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d2.bias, openmc.stats.Uniform)
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samples, weights = d2.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, exp_mean)
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assert np.all(weights != 1.0)
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@pytest.mark.flaky(reruns=1)
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def test_maxwell():
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theta = 1.2895e6
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d = openmc.stats.Maxwell(theta)
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elem = d.to_xml_element('distribution')
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d = openmc.stats.Maxwell.from_xml_element(elem)
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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 and check that the mean of the samples is
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# within 4 std. dev. of the expected mean
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n_samples = 1_000_000
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, exp_mean)
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assert np.all(weights == 1.0)
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# A second sample starting from a different seed
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samples_2, weights_2 = d.sample(n_samples)
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assert_sample_mean(samples_2, exp_mean)
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assert samples_2.mean() != samples.mean()
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assert np.all(weights_2 == 1)
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# Test biased distribution
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d.bias = openmc.stats.Maxwell((theta * 1.1))
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bias_elem = d.to_xml_element('distribution')
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d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d2.bias, openmc.stats.Maxwell)
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samples, weights = d2.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, exp_mean)
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assert np.all(weights != 1.0)
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@pytest.mark.flaky(reruns=1)
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def test_watt():
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a, b = 0.965e6, 2.29e-6
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d = openmc.stats.Watt(a, b)
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elem = d.to_xml_element('distribution')
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d = openmc.stats.Watt.from_xml_element(elem)
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assert d.a == a
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assert d.b == b
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assert len(d) == 2
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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 and check that the mean of the samples is within
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# 4 std. dev. of the expected mean
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n_samples = 1_000_000
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, exp_mean)
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assert np.all(weights == 1.0)
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# Test biased distribution with 5 percent higher T_e
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d.bias = openmc.stats.Watt(a*1.05, b)
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bias_elem = d.to_xml_element('distribution')
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d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d2.bias, openmc.stats.Watt)
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samples, weights = d2.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, exp_mean)
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assert np.all(weights != 1.0)
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@pytest.mark.flaky(reruns=1)
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def test_tabular():
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# test linear-linear sampling
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x = np.array([0.001, 5.0, 7.0, 10.0])
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p = np.array([10.0, 20.0, 5.0, 6.0])
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d = openmc.stats.Tabular(x, p, 'linear-linear')
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n_samples = 100_000
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, d.mean())
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assert np.all(weights == 1.0)
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for scheme in _INTERPOLATION_SCHEMES:
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# test sampling
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d = openmc.stats.Tabular(x, p, scheme)
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n_samples = 100_000
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samples = d.sample(n_samples)[0]
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assert_sample_mean(samples, d.mean())
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# test histogram sampling
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d = openmc.stats.Tabular(x, p, interpolation='histogram')
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samples, weights = d.sample(n_samples)
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assert_sample_mean(samples, d.mean())
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assert np.all(weights == 1.0)
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# Multiplying the probabilities should preserve the mean but change the integral
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d2 = openmc.stats.Tabular(x, p*2, interpolation='histogram')
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assert d2.mean() == pytest.approx(d.mean())
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assert d2.integral() == pytest.approx(2.0*d.integral())
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# Normalizing should result in an integral of 1
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d.normalize()
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assert d.integral() == pytest.approx(1.0)
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# ensure that passing a set of probabilities shorter than x works
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# for histogram interpolation
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d = openmc.stats.Tabular(x, p[:-1], interpolation='histogram')
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d.cdf()
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d.mean()
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assert_sample_mean(d.sample(n_samples)[0], d.mean())
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# passing a shorter probability set should raise an error for linear-linear
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with pytest.raises(ValueError):
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d = openmc.stats.Tabular(x, p[:-1], interpolation='linear-linear')
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d.cdf()
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# Use probabilities of correct length for linear-linear interpolation and
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# call the CDF method
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d = openmc.stats.Tabular(x, p, interpolation='linear-linear')
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d.cdf()
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# Test biased distribution
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d.bias = openmc.stats.Uniform(x[0], x[-1])
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bias_elem = d.to_xml_element('distribution')
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d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d2.bias, openmc.stats.Uniform)
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samples, weights = d2.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, d2.mean())
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assert np.all(weights != 1.0)
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def test_tabular_from_xml():
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x = np.array([0.0, 5.0, 7.0, 10.0])
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p = np.array([10.0, 20.0, 5.0, 6.0])
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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 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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# Make sure XML roundtrip works with len(x) == len(p) + 1
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x = np.array([0.0, 5.0, 7.0, 10.0])
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p = np.array([10.0, 20.0, 5.0])
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d = openmc.stats.Tabular(x, p, 'histogram')
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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 all(d.x == x)
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assert all(d.p == p)
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def test_legendre():
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# Pu239 elastic scattering at 100 keV
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coeffs = [1.000e+0, 1.536e-1, 1.772e-2, 5.945e-4, 3.497e-5, 1.881e-5]
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d = openmc.stats.Legendre(coeffs)
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assert d.coefficients == pytest.approx(coeffs)
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assert len(d) == len(coeffs)
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# Integrating distribution should yield one
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mu = np.linspace(-1., 1., 1000)
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assert trapezoid(d(mu), mu) == pytest.approx(1.0, rel=1e-4)
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with pytest.raises(NotImplementedError):
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d.to_xml_element('distribution')
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@pytest.mark.flaky(reruns=1)
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def test_mixture():
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d1 = openmc.stats.Uniform(0, 5)
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d2 = openmc.stats.Uniform(3, 7)
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p = [0.5, 0.5]
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mix = openmc.stats.Mixture(p, [d1, d2])
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np.testing.assert_allclose(mix.probability, p)
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assert mix.distribution == [d1, d2]
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assert len(mix) == 4
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# Sample and make sure sample mean is close to expected mean
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n_samples = 1_000_000
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samples, weights = mix.sample(n_samples)
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assert_sample_mean(samples, (2.5 + 5.0)/2)
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assert np.all(weights == 1.0)
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elem = mix.to_xml_element('distribution')
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d = openmc.stats.Mixture.from_xml_element(elem)
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np.testing.assert_allclose(d.probability, p)
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assert d.distribution == [d1, d2]
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assert len(d) == 4
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# Test biased sub-distribution
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d.distribution[0].bias = openmc.stats.PowerLaw(0, 5, 2)
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bias_elem = d.to_xml_element('distribution')
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d3 = openmc.stats.Univariate.from_xml_element(bias_elem)
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assert isinstance (d3.distribution[0].bias, openmc.stats.PowerLaw)
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samples, weights = d3.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, (2.5 + 5.0)/2)
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# Test biased meta-probability
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d.distribution[0].bias = None
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d.bias = [0.25, 0.75]
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bias_elem_2 = d.to_xml_element('distribution')
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d4 = openmc.stats.Univariate.from_xml_element(bias_elem_2)
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assert isinstance (d4.bias, np.ndarray)
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samples, weights = d4.sample(n_samples)
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weighted_sample = samples * weights
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assert_sample_mean(weighted_sample, (2.5 + 5.0)/2)
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assert np.all(weights != 1.0)
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def test_mixture_clip():
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# Create mixture distribution containing a discrete distribution with two
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# points that are not important, one because the x value is very small, and
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# one because the p value is very small
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d1 = openmc.stats.Discrete([1e-8, 1.0, 2.0, 1000.0], [3.0, 2.0, 5.0, 1e-12])
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d2 = openmc.stats.Uniform(0, 5)
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mix = openmc.stats.Mixture([0.5, 0.5], [d1, d2])
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# Clipping should reduce the contained discrete distribution to 2 points
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mix_clip = mix.clip(1e-6)
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assert mix_clip.distribution[0].x.size == 2
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assert mix_clip.distribution[0].p.size == 2
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# Make sure inplace returns same object
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mix_same = mix.clip(1e-6, inplace=True)
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assert mix_same is mix
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# Make sure clip removes low probability distributions
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d_small = openmc.stats.Uniform(0., 1.)
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d_large = openmc.stats.Uniform(2., 5.)
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mix = openmc.stats.Mixture([1e-10, 1.0], [d_small, d_large])
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mix_clip = mix.clip(1e-3)
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assert mix_clip.distribution == [d_large]
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# Make sure warning is raised if tolerance is exceeded
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d1 = openmc.stats.Discrete([1.0, 1.001], [1.0, 0.7e-6])
|
|
d2 = openmc.stats.Tabular([0.0, 1.0], [0.7e-6], interpolation='histogram')
|
|
mix = openmc.stats.Mixture([1.0, 1.0], [d1, d2])
|
|
with pytest.warns(UserWarning):
|
|
mix_clip = mix.clip(1e-6)
|
|
|
|
# Make sure warning is raised if a biased Discrete is clipped
|
|
d3 = openmc.stats.Discrete([1.0, 1.001], [1.0, 0.7e-8])
|
|
d3.bias = [0.9, 0.1]
|
|
mix = openmc.stats.Mixture([1.0, 1.0], [d3, d2])
|
|
with pytest.raises(RuntimeError):
|
|
mix_clip = mix.clip(1e-6)
|
|
|
|
|
|
def test_polar_azimuthal():
|
|
# default polar-azimuthal should be uniform in mu and phi
|
|
d = openmc.stats.PolarAzimuthal()
|
|
assert isinstance(d.mu, openmc.stats.Uniform)
|
|
assert d.mu.a == -1.
|
|
assert d.mu.b == 1.
|
|
assert isinstance(d.phi, openmc.stats.Uniform)
|
|
assert d.phi.a == 0.
|
|
assert d.phi.b == 2*pi
|
|
|
|
mu = openmc.stats.Discrete(1., 1.)
|
|
phi = openmc.stats.Discrete(0., 1.)
|
|
d = openmc.stats.PolarAzimuthal(mu, phi)
|
|
assert d.mu == mu
|
|
assert d.phi == phi
|
|
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'angle'
|
|
assert elem.attrib['type'] == 'mu-phi'
|
|
assert elem.find('mu') is not None
|
|
assert elem.find('phi') is not None
|
|
|
|
d = openmc.stats.PolarAzimuthal.from_xml_element(elem)
|
|
assert d.mu.x == [1.]
|
|
assert d.mu.p == [1.]
|
|
assert d.phi.x == [0.]
|
|
assert d.phi.p == [1.]
|
|
|
|
d = openmc.stats.UnitSphere.from_xml_element(elem)
|
|
assert isinstance(d, openmc.stats.PolarAzimuthal)
|
|
|
|
|
|
def test_isotropic():
|
|
d = openmc.stats.Isotropic()
|
|
mu = openmc.stats.Uniform(-1.0, 1.0)
|
|
phi = openmc.stats.PowerLaw(0., 2*np.pi, 2)
|
|
d2 = openmc.stats.PolarAzimuthal(mu, phi)
|
|
d.bias = d2
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'angle'
|
|
assert elem.attrib['type'] == 'isotropic'
|
|
|
|
d = openmc.stats.Isotropic.from_xml_element(elem)
|
|
assert isinstance(d, openmc.stats.Isotropic)
|
|
assert isinstance(d.bias, openmc.stats.PolarAzimuthal)
|
|
|
|
|
|
def test_monodirectional():
|
|
d = openmc.stats.Monodirectional((1., 0., 0.))
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'angle'
|
|
assert elem.attrib['type'] == 'monodirectional'
|
|
|
|
d = openmc.stats.Monodirectional.from_xml_element(elem)
|
|
assert d.reference_uvw == pytest.approx((1., 0., 0.))
|
|
|
|
|
|
def test_cartesian():
|
|
x = openmc.stats.Uniform(-10., 10.)
|
|
y = openmc.stats.Uniform(-10., 10.)
|
|
z = openmc.stats.Uniform(0., 20.)
|
|
z.bias = openmc.stats.PowerLaw(0., 20., 3)
|
|
d = openmc.stats.CartesianIndependent(x, y, z)
|
|
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'space'
|
|
assert elem.attrib['type'] == 'cartesian'
|
|
assert elem.find('x') is not None
|
|
assert elem.find('y') is not None
|
|
|
|
d = openmc.stats.CartesianIndependent.from_xml_element(elem)
|
|
assert d.x == x
|
|
assert d.y == y
|
|
assert d.z == z
|
|
|
|
d = openmc.stats.Spatial.from_xml_element(elem)
|
|
assert isinstance(d, openmc.stats.CartesianIndependent)
|
|
assert isinstance (d.z.bias, openmc.stats.PowerLaw)
|
|
|
|
|
|
def test_box():
|
|
lower_left = (-10., -10., -10.)
|
|
upper_right = (10., 10., 10.)
|
|
d = openmc.stats.Box(lower_left, upper_right)
|
|
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'space'
|
|
assert elem.attrib['type'] == 'box'
|
|
assert elem.find('parameters') is not None
|
|
|
|
d = openmc.stats.Box.from_xml_element(elem)
|
|
assert d.lower_left == pytest.approx(lower_left)
|
|
assert d.upper_right == pytest.approx(upper_right)
|
|
|
|
|
|
def test_point():
|
|
p = (-4., 2., 10.)
|
|
d = openmc.stats.Point(p)
|
|
|
|
elem = d.to_xml_element()
|
|
assert elem.tag == 'space'
|
|
assert elem.attrib['type'] == 'point'
|
|
assert elem.find('parameters') is not None
|
|
|
|
d = openmc.stats.Point.from_xml_element(elem)
|
|
assert d.xyz == pytest.approx(p)
|
|
|
|
|
|
def test_spherical_uniform():
|
|
r_outer = 2.0
|
|
r_inner = 1.0
|
|
thetas = (0.0, pi/2)
|
|
phis = (0.0, pi)
|
|
origin = (0.0, 1.0, 2.0)
|
|
|
|
sph_indep_function = openmc.stats.spherical_uniform(r_outer,
|
|
r_inner,
|
|
thetas,
|
|
phis,
|
|
origin)
|
|
|
|
assert isinstance(sph_indep_function, openmc.stats.SphericalIndependent)
|
|
|
|
|
|
def test_cylindrical_uniform():
|
|
r_outer = 2.0
|
|
r_inner = 1.0
|
|
height = 1.0
|
|
phis = (0.0, pi)
|
|
origin = (0.0, 1.0, 2.0)
|
|
|
|
dist = openmc.stats.cylindrical_uniform(r_outer, height, r_inner, phis,
|
|
origin=origin)
|
|
|
|
assert isinstance(dist, openmc.stats.CylindricalIndependent)
|
|
|
|
# Check r distribution (PowerLaw with exponent 1 for uniform area sampling)
|
|
assert isinstance(dist.r, openmc.stats.PowerLaw)
|
|
assert dist.r.a == pytest.approx(r_inner)
|
|
assert dist.r.b == pytest.approx(r_outer)
|
|
assert dist.r.n == pytest.approx(1.0)
|
|
|
|
# Check phi distribution
|
|
assert isinstance(dist.phi, openmc.stats.Uniform)
|
|
assert dist.phi.a == pytest.approx(phis[0])
|
|
assert dist.phi.b == pytest.approx(phis[1])
|
|
|
|
# Check z distribution (centered on origin along z_dir)
|
|
assert isinstance(dist.z, openmc.stats.Uniform)
|
|
assert dist.z.a == pytest.approx(-height / 2)
|
|
assert dist.z.b == pytest.approx(height / 2)
|
|
|
|
# Check origin and default directions
|
|
np.testing.assert_allclose(dist.origin, origin)
|
|
np.testing.assert_allclose(dist.r_dir, [1., 0., 0.])
|
|
np.testing.assert_allclose(dist.z_dir, [0., 0., 1.])
|
|
|
|
# XML round-trip preserves all parameters
|
|
elem = dist.to_xml_element()
|
|
dist2 = openmc.stats.CylindricalIndependent.from_xml_element(elem)
|
|
np.testing.assert_allclose(dist2.origin, origin)
|
|
np.testing.assert_allclose(dist2.r_dir, dist.r_dir)
|
|
np.testing.assert_allclose(dist2.z_dir, dist.z_dir)
|
|
|
|
|
|
def test_cylindrical_uniform_tilted():
|
|
# Test with non-default axis orientation (y-axis as cylinder axis)
|
|
dist = openmc.stats.cylindrical_uniform(
|
|
r_outer=3.0, height=2.0, r_dir=(1., 0., 0.), z_dir=(0., 1., 0.)
|
|
)
|
|
np.testing.assert_allclose(dist.z_dir, [0., 1., 0.])
|
|
np.testing.assert_allclose(dist.r_dir, [1., 0., 0.])
|
|
|
|
# XML round-trip preserves tilted directions
|
|
elem = dist.to_xml_element()
|
|
dist2 = openmc.stats.CylindricalIndependent.from_xml_element(elem)
|
|
np.testing.assert_allclose(dist2.z_dir, dist.z_dir)
|
|
np.testing.assert_allclose(dist2.r_dir, dist.r_dir)
|
|
|
|
|
|
def test_cylindrical_uniform_ring():
|
|
# height=0 should produce a flat ring (delta function at z=0)
|
|
r_outer = 2.0
|
|
r_inner = 1.0
|
|
phis = (0.0, pi)
|
|
origin = (0.0, 1.0, 2.0)
|
|
|
|
dist = openmc.stats.cylindrical_uniform(r_outer, 0.0, r_inner, phis,
|
|
origin=origin)
|
|
|
|
assert isinstance(dist, openmc.stats.CylindricalIndependent)
|
|
|
|
# Check r distribution
|
|
assert isinstance(dist.r, openmc.stats.PowerLaw)
|
|
assert dist.r.a == pytest.approx(r_inner)
|
|
assert dist.r.b == pytest.approx(r_outer)
|
|
assert dist.r.n == pytest.approx(1.0)
|
|
|
|
# Check phi distribution
|
|
assert isinstance(dist.phi, openmc.stats.Uniform)
|
|
assert dist.phi.a == pytest.approx(phis[0])
|
|
assert dist.phi.b == pytest.approx(phis[1])
|
|
|
|
# z distribution must be a delta function at 0.0 (local frame)
|
|
assert isinstance(dist.z, openmc.stats.Discrete)
|
|
assert dist.z.x[0] == pytest.approx(0.0)
|
|
|
|
# XML round-trip
|
|
elem = dist.to_xml_element()
|
|
dist2 = openmc.stats.CylindricalIndependent.from_xml_element(elem)
|
|
np.testing.assert_allclose(dist2.origin, origin)
|
|
np.testing.assert_allclose(dist2.r_dir, dist.r_dir)
|
|
np.testing.assert_allclose(dist2.z_dir, dist.z_dir)
|
|
|
|
|
|
@pytest.mark.flaky(reruns=1)
|
|
def test_normal():
|
|
mean = 10.0
|
|
std_dev = 2.0
|
|
d = openmc.stats.Normal(mean,std_dev)
|
|
|
|
elem = d.to_xml_element('distribution')
|
|
assert elem.attrib['type'] == 'normal'
|
|
|
|
d = openmc.stats.Normal.from_xml_element(elem)
|
|
assert d.mean_value == pytest.approx(mean)
|
|
assert d.std_dev == pytest.approx(std_dev)
|
|
assert len(d) == 2
|
|
|
|
# sample normal distribution
|
|
n_samples = 100_000
|
|
samples, weights = d.sample(n_samples)
|
|
assert_sample_mean(samples, mean)
|
|
assert np.all(weights == 1.0)
|
|
|
|
# Test biased distribution
|
|
d.bias = openmc.stats.Normal(10.0, 4.0)
|
|
bias_elem = d.to_xml_element('distribution')
|
|
d2 = openmc.stats.Univariate.from_xml_element(bias_elem)
|
|
assert isinstance (d2.bias, openmc.stats.Normal)
|
|
samples, weights = d2.sample(n_samples)
|
|
weighted_sample = samples * weights
|
|
assert_sample_mean(weighted_sample, mean)
|
|
assert np.all(weights != 1.0)
|
|
|
|
|
|
@pytest.mark.flaky(reruns=1)
|
|
def test_normal_truncated():
|
|
mean = 10.0
|
|
std_dev = 2.0
|
|
lower = 6.0
|
|
upper = 14.0
|
|
|
|
d = openmc.stats.Normal(mean, std_dev, lower, upper)
|
|
|
|
# Check attributes
|
|
assert d.mean_value == pytest.approx(mean)
|
|
assert d.std_dev == pytest.approx(std_dev)
|
|
assert d.lower == pytest.approx(lower)
|
|
assert d.upper == pytest.approx(upper)
|
|
assert len(d) == 4
|
|
assert d.support == (lower, upper)
|
|
|
|
# Test XML round-trip
|
|
elem = d.to_xml_element('distribution')
|
|
assert elem.attrib['type'] == 'normal'
|
|
params = elem.attrib['parameters'].split()
|
|
assert len(params) == 4
|
|
|
|
d2 = openmc.stats.Normal.from_xml_element(elem)
|
|
assert d2.mean_value == pytest.approx(mean)
|
|
assert d2.std_dev == pytest.approx(std_dev)
|
|
assert d2.lower == pytest.approx(lower)
|
|
assert d2.upper == pytest.approx(upper)
|
|
|
|
# Test PDF evaluation
|
|
# PDF should be zero outside bounds
|
|
assert d.evaluate(lower - 1.0) == 0.0
|
|
assert d.evaluate(upper + 1.0) == 0.0
|
|
|
|
# PDF should be positive inside bounds
|
|
assert d.evaluate(mean) > 0.0
|
|
|
|
# PDF should be higher than untruncated at the mean (due to renormalization)
|
|
d_unbounded = openmc.stats.Normal(mean, std_dev)
|
|
assert d.evaluate(mean) > d_unbounded.evaluate(mean)
|
|
|
|
# Verify that PDF integrates to approximately 1
|
|
x = np.linspace(lower, upper, 1000)
|
|
integral = trapezoid(d.evaluate(x), x)
|
|
assert integral == pytest.approx(1.0, rel=0.01)
|
|
|
|
# Sample truncated distribution
|
|
n_samples = 10_000
|
|
samples, weights = d.sample(n_samples)
|
|
|
|
# All samples should be within bounds
|
|
assert np.all(samples >= lower)
|
|
assert np.all(samples <= upper)
|
|
|
|
# Weights should all be 1 (no biasing)
|
|
assert np.all(weights == 1.0)
|
|
|
|
|
|
def test_normal_truncated_one_sided():
|
|
# Test lower-bounded only (positive half-normal centered at 0)
|
|
d_lower = openmc.stats.Normal(0.0, 1.0, lower=0.0)
|
|
assert d_lower.lower == 0.0
|
|
assert d_lower.upper == np.inf
|
|
assert d_lower.evaluate(-1.0) == 0.0
|
|
assert d_lower.evaluate(1.0) > 0.0
|
|
|
|
# PDF at 0 should be approximately 2 * 0.3989 ≈ 0.798 (half-normal)
|
|
assert d_lower.evaluate(0.0) == pytest.approx(0.798, rel=0.01)
|
|
|
|
# Test upper-bounded only
|
|
d_upper = openmc.stats.Normal(0.0, 1.0, upper=0.0)
|
|
assert d_upper.lower == -np.inf
|
|
assert d_upper.upper == 0.0
|
|
assert d_upper.evaluate(1.0) == 0.0
|
|
assert d_upper.evaluate(-1.0) > 0.0
|
|
|
|
|
|
def test_normal_truncated_errors():
|
|
# Invalid bounds (lower >= upper)
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.Normal(0.0, 1.0, lower=1.0, upper=0.0)
|
|
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.Normal(0.0, 1.0, lower=1.0, upper=1.0)
|
|
|
|
|
|
@pytest.mark.flaky(reruns=1)
|
|
def test_muir():
|
|
mean = 10.0
|
|
mass = 5.0
|
|
temp = 20000.
|
|
d = openmc.stats.muir(mean, mass, temp)
|
|
assert isinstance(d, openmc.stats.Normal)
|
|
|
|
elem = d.to_xml_element('energy')
|
|
assert elem.attrib['type'] == 'normal'
|
|
|
|
d = openmc.stats.Univariate.from_xml_element(elem)
|
|
assert isinstance(d, openmc.stats.Normal)
|
|
|
|
# sample muir distribution
|
|
n_samples = 100_000
|
|
samples, weights = d.sample(n_samples)
|
|
assert_sample_mean(samples, mean)
|
|
assert np.all(weights == 1.0)
|
|
|
|
|
|
@pytest.mark.flaky(reruns=1)
|
|
def test_combine_distributions():
|
|
# Combine two discrete (same data as in test_merge_discrete)
|
|
x1 = [0.0, 1.0, 10.0]
|
|
p1 = [0.3, 0.2, 0.5]
|
|
d1 = openmc.stats.Discrete(x1, p1)
|
|
x2 = [0.5, 1.0, 5.0]
|
|
p2 = [0.4, 0.5, 0.1]
|
|
d2 = openmc.stats.Discrete(x2, p2)
|
|
|
|
# Merged distribution should have x values sorted and probabilities
|
|
# appropriately combined. Duplicate x values should appear once.
|
|
merged = openmc.stats.combine_distributions([d1, d2], [0.6, 0.4])
|
|
assert isinstance(merged, openmc.stats.Discrete)
|
|
assert merged.x == pytest.approx([0.0, 0.5, 1.0, 5.0, 10.0])
|
|
assert merged.p == pytest.approx(
|
|
[0.6*0.3, 0.4*0.4, 0.6*0.2 + 0.4*0.5, 0.4*0.1, 0.6*0.5])
|
|
|
|
# Probabilities add up but are not normalized
|
|
d1 = openmc.stats.Discrete([3.0], [1.0])
|
|
triple = openmc.stats.combine_distributions([d1, d1, d1], [1.0, 2.0, 3.0])
|
|
assert triple.x == pytest.approx([3.0])
|
|
assert triple.p == pytest.approx([6.0])
|
|
|
|
# Combine discrete and tabular
|
|
t1 = openmc.stats.Tabular(x2, p2)
|
|
mixed = openmc.stats.combine_distributions([d1, t1], [0.5, 0.5])
|
|
assert isinstance(mixed, openmc.stats.Mixture)
|
|
assert len(mixed.distribution) == 2
|
|
assert len(mixed.probability) == 2
|
|
assert mixed == openmc.stats.combine_distributions([mixed], [1.0])
|
|
|
|
# Mixture combined with another distribution: probabilities should be
|
|
# correctly scaled when the Mixture is flattened
|
|
d_a = openmc.stats.delta_function(1.0)
|
|
d_b = openmc.stats.delta_function(2.0)
|
|
m = openmc.stats.Mixture([0.3, 0.7], [d_a, d_b])
|
|
extra = openmc.stats.delta_function(3.0)
|
|
result = openmc.stats.combine_distributions([m, extra], [0.5, 0.5])
|
|
assert isinstance(result, openmc.stats.Discrete)
|
|
assert result.x == pytest.approx([1.0, 2.0, 3.0])
|
|
assert result.p == pytest.approx([0.5*0.3, 0.5*0.7, 0.5])
|
|
|
|
# Passing a Mixture with a bias should warn that the bias is dropped
|
|
biased_m = openmc.stats.Mixture([0.5, 0.5], [d_a, d_b], bias=[0.8, 0.2])
|
|
with pytest.warns(UserWarning, match='bias'):
|
|
openmc.stats.combine_distributions([biased_m], [1.0])
|
|
|
|
# Single tabular returns a tabular distribution with scaled probabilities
|
|
t_single = openmc.stats.Tabular([0.0, 1.0], [2.0, 0.0])
|
|
scaled = openmc.stats.combine_distributions([t_single], [0.25])
|
|
assert isinstance(scaled, openmc.stats.Tabular)
|
|
assert scaled.p == pytest.approx([0.5, 0.0])
|
|
|
|
# Mixture with biased tabular should preserve unbiased mean via weights
|
|
bias = openmc.stats.Tabular([0.0, 1.0], [2.0, 0.0])
|
|
t_biased = openmc.stats.Tabular([0.0, 1.0], [1.0, 1.0], bias=bias)
|
|
d1 = openmc.stats.delta_function(0.0)
|
|
mixed = openmc.stats.combine_distributions([t_biased, d1], [0.5, 0.5])
|
|
assert isinstance(mixed, openmc.stats.Mixture)
|
|
samples, weights = mixed.sample(10_000)
|
|
assert_sample_mean(samples*weights, 0.25)
|
|
|
|
# Combine 1 discrete and 2 tabular -- the tabular distributions should
|
|
# combine to produce a uniform distribution with mean 0.5. The combined
|
|
# distribution should have a mean of 0.25.
|
|
t1 = openmc.stats.Tabular([0., 1.], [2.0, 0.0])
|
|
t2 = openmc.stats.Tabular([0., 1.], [0.0, 2.0])
|
|
d1 = openmc.stats.delta_function(0.0)
|
|
combined = openmc.stats.combine_distributions([t1, t2, d1], [0.25, 0.25, 0.5])
|
|
assert combined.integral() == pytest.approx(1.0)
|
|
|
|
# Sample the combined distribution and make sure the sample mean is within
|
|
# uncertainty of the expected value
|
|
samples, weights = combined.sample(10_000)
|
|
assert_sample_mean(samples, 0.25)
|
|
assert np.all(weights == 1.0)
|
|
|
|
# If biased/unbiased Discrete distributions are combined, unbiased probability
|
|
# should be conserved and points from both original distributions should be
|
|
# assigned bias probabilities.
|
|
x1 = [0.0, 1.0, 10.0]
|
|
p1 = [0.3, 0.2, 0.5]
|
|
b1 = [0.2, 0.5, 0.3]
|
|
d1 = openmc.stats.Discrete(x1, p1, b1)
|
|
x2 = [0.5, 1.0, 5.0]
|
|
p2 = [0.4, 0.5, 0.1]
|
|
d2 = openmc.stats.Discrete(x2, p2)
|
|
combined = openmc.stats.combine_distributions([d1, d2, t1], [0.25, 0.25, 0.5])
|
|
|
|
p3 = [0.075, 0.1, 0.175, 0.025, 0.125]
|
|
b3 = [0.05, 0.1, 0.25, 0.025, 0.075]
|
|
assert all(combined.distribution[-1].p == p3)
|
|
assert all(combined.distribution[-1].bias == b3)
|
|
|
|
|
|
def test_reference_vwu_projection():
|
|
"""When a non-orthogonal vector is provided, the setter should project out
|
|
any component along reference_uvw so the stored vector is orthogonal.
|
|
"""
|
|
pa = openmc.stats.PolarAzimuthal() # default reference_uvw == (0, 0, 1)
|
|
|
|
# Provide a vector that is not orthogonal to (0,0,1)
|
|
pa.reference_vwu = (2.0, 0.5, 0.3)
|
|
|
|
reference_v = np.asarray(pa.reference_vwu)
|
|
reference_u = np.asarray(pa.reference_uvw)
|
|
|
|
# reference_v should be orthogonal to reference_u
|
|
assert abs(np.dot(reference_v, reference_u)) < 1e-6
|
|
|
|
|
|
def test_reference_vwu_normalization():
|
|
"""When a non-normalized vector is provided, the setter should normalize
|
|
the projected vector to unit length.
|
|
"""
|
|
pa = openmc.stats.PolarAzimuthal() # default reference_uvw == (0, 0, 1)
|
|
|
|
# Provide a vector that is neither orthogonal to (0,0,1) nor unit-length
|
|
pa.reference_vwu = (2.0, 0.5, 0.3)
|
|
|
|
reference_v = np.asarray(pa.reference_vwu)
|
|
|
|
# reference_v should be unit length
|
|
assert np.isclose(np.linalg.norm(reference_v), 1.0, atol=1e-12)
|
|
|
|
|
|
def test_fusion_spectrum_dd():
|
|
d = openmc.stats.fusion_neutron_spectrum(10e3, 'DD')
|
|
assert isinstance(d, openmc.stats.Normal)
|
|
|
|
# E_0 for D(d,n)3He is ~2.45 MeV; thermal shift at 10 keV should be
|
|
# several tens of keV, so mean should be noticeably above E_0
|
|
assert d.mean_value > 2.45e6
|
|
assert d.mean_value < 2.6e6
|
|
|
|
# Standard deviation should be positive and on order of ~50-100 keV
|
|
assert d.std_dev > 30e3
|
|
assert d.std_dev < 200e3
|
|
|
|
|
|
def test_fusion_spectrum_dt():
|
|
d = openmc.stats.fusion_neutron_spectrum(10e3, 'DT')
|
|
assert isinstance(d, openmc.stats.Normal)
|
|
|
|
# E_0 for T(d,n)alpha is ~14.02 MeV; with thermal shift mean should be
|
|
# above E_0 by several tens of keV
|
|
assert d.mean_value > 14.02e6
|
|
assert d.mean_value < 14.2e6
|
|
|
|
# Standard deviation should be on order of ~200-400 keV
|
|
assert d.std_dev > 100e3
|
|
assert d.std_dev < 500e3
|
|
|
|
|
|
def test_fusion_spectrum_temp_continuity():
|
|
# Verify the low-T and high-T formulas produce nearly identical results
|
|
# at the 40 keV switchover point
|
|
d_lo = openmc.stats.fusion_neutron_spectrum(39.99e3, 'DT')
|
|
d_hi = openmc.stats.fusion_neutron_spectrum(40.01e3, 'DT')
|
|
|
|
assert d_lo.mean_value == pytest.approx(d_hi.mean_value, rel=1e-3)
|
|
assert d_lo.std_dev == pytest.approx(d_hi.std_dev, rel=1e-3)
|
|
|
|
# Same check for DD
|
|
d_lo = openmc.stats.fusion_neutron_spectrum(39.99e3, 'DD')
|
|
d_hi = openmc.stats.fusion_neutron_spectrum(40.01e3, 'DD')
|
|
|
|
assert d_lo.mean_value == pytest.approx(d_hi.mean_value, rel=1e-3)
|
|
assert d_lo.std_dev == pytest.approx(d_hi.std_dev, rel=1e-3)
|
|
|
|
|
|
def test_fusion_spectrum_high_temp():
|
|
# At T_i = 80 keV (high-T regime), ensure the function still produces
|
|
# reasonable results using Table IV formulas
|
|
for reactants in ('DD', 'DT'):
|
|
d = openmc.stats.fusion_neutron_spectrum(80e3, reactants)
|
|
assert isinstance(d, openmc.stats.Normal)
|
|
assert d.mean_value > 0
|
|
assert d.std_dev > 0
|
|
|
|
# DT mean at 80 keV should be higher than at 10 keV
|
|
d_10 = openmc.stats.fusion_neutron_spectrum(10e3, 'DT')
|
|
d_80 = openmc.stats.fusion_neutron_spectrum(80e3, 'DT')
|
|
assert d_80.mean_value > d_10.mean_value
|
|
assert d_80.std_dev > d_10.std_dev
|
|
|
|
|
|
def test_fusion_spectrum_zero_temp():
|
|
# At very low temperature, mean should approach E_0 and width should
|
|
# approach zero
|
|
d = openmc.stats.fusion_neutron_spectrum(1.0, 'DT')
|
|
assert d.mean_value == pytest.approx(14.049e6, rel=1e-3)
|
|
assert d.std_dev < 5e3 # width approaches zero at low temperature
|
|
|
|
|
|
def test_fusion_spectrum_invalid():
|
|
# Invalid reactant string should raise an error
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.fusion_neutron_spectrum(10e3, '🐔🧇')
|
|
|
|
# Negative temperature should raise an error
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.fusion_neutron_spectrum(-10e3, 'DT')
|
|
|
|
# Temperature above 100 keV should raise an error
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.fusion_neutron_spectrum(101e3, 'DT')
|
|
|
|
|
|
@pytest.fixture(autouse=False)
|
|
def decay_spectrum_chain():
|
|
"""Set chain_file for the duration of a test and clear the _photon_integral
|
|
cache so results from a different chain don't bleed across tests."""
|
|
CHAIN_FILE = (Path(__file__).parents[1] / 'chain_simple.xml').resolve()
|
|
DecaySpectrum._photon_integral.cache_clear()
|
|
with openmc.config.patch('chain_file', CHAIN_FILE):
|
|
yield
|
|
DecaySpectrum._photon_integral.cache_clear()
|
|
|
|
|
|
def test_decay_spectrum_construction():
|
|
nuclides = {'I135': 1.5e-3, 'Xe135': 8.2e-4}
|
|
d = openmc.stats.DecaySpectrum(nuclides, volume=100.0)
|
|
assert d.nuclides == nuclides
|
|
assert d.volume == pytest.approx(100.0)
|
|
assert len(d) == 2
|
|
|
|
|
|
def test_decay_spectrum_validation():
|
|
# nuclides must be a dict
|
|
with pytest.raises(TypeError):
|
|
openmc.stats.DecaySpectrum(['I135'], volume=1.0)
|
|
|
|
# densities must be > 0
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.DecaySpectrum({'I135': -1.0}, volume=1.0)
|
|
|
|
# volume must be > 0
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.DecaySpectrum({'I135': 1e-3}, volume=-1.0)
|
|
|
|
with pytest.raises(ValueError):
|
|
openmc.stats.DecaySpectrum({'I135': 1e-3}, volume=0.0)
|
|
|
|
|
|
def test_decay_spectrum_xml_roundtrip():
|
|
nuclides = {'I135': 1.5e-3, 'Xe135': 8.2e-4}
|
|
d = openmc.stats.DecaySpectrum(nuclides, volume=100.0)
|
|
|
|
elem = d.to_xml_element('energy')
|
|
assert elem.get('type') == 'decay_spectrum'
|
|
assert float(elem.get('volume')) == pytest.approx(100.0)
|
|
assert elem.findtext('nuclides').split() == list(nuclides)
|
|
assert [float(x) for x in elem.findtext('parameters').split()] == pytest.approx(
|
|
list(nuclides.values()))
|
|
|
|
# Round-trip via DecaySpectrum.from_xml_element
|
|
d2 = openmc.stats.DecaySpectrum.from_xml_element(elem)
|
|
assert d2.nuclides == nuclides
|
|
assert d2.volume == pytest.approx(100.0)
|
|
|
|
# Round-trip via the Univariate dispatcher
|
|
d3 = openmc.stats.Univariate.from_xml_element(elem)
|
|
assert isinstance(d3, openmc.stats.DecaySpectrum)
|
|
assert d3 == d
|
|
|
|
|
|
def test_decay_spectrum_to_distribution(decay_spectrum_chain):
|
|
# Single emitting nuclide -> concrete distribution, not None
|
|
d = openmc.stats.DecaySpectrum({'I135': 1e-3}, volume=10.0)
|
|
dist = d.to_distribution()
|
|
assert dist is not None
|
|
|
|
# Result is cached on second call
|
|
dist2 = d.to_distribution()
|
|
assert dist2 is dist
|
|
|
|
# Nuclide with no photon source -> None
|
|
d_stable = openmc.stats.DecaySpectrum({'Xe136': 1e-3}, volume=10.0)
|
|
assert d_stable.to_distribution() is None
|
|
|
|
# Mixture of emitters -> non-None combined distribution
|
|
d_mix = openmc.stats.DecaySpectrum(
|
|
{'I135': 1e-3, 'Xe135': 5e-4}, volume=10.0
|
|
)
|
|
dist_mix = d_mix.to_distribution()
|
|
assert dist_mix is not None
|
|
|
|
|
|
def test_decay_spectrum_integral(decay_spectrum_chain):
|
|
# For an emitting nuclide, integral should be > 0
|
|
d = openmc.stats.DecaySpectrum({'I135': 1e-3}, volume=10.0)
|
|
assert d.integral() > 0.0
|
|
|
|
# Proportional to density: doubling density doubles integral
|
|
d2 = openmc.stats.DecaySpectrum({'I135': 2e-3}, volume=10.0)
|
|
assert d2.integral() == pytest.approx(2.0 * d.integral())
|
|
|
|
# Proportional to volume
|
|
d3 = openmc.stats.DecaySpectrum({'I135': 1e-3}, volume=20.0)
|
|
assert d3.integral() == pytest.approx(2.0 * d.integral())
|
|
|
|
# Pure non-emitter -> 0.0
|
|
d_stable = openmc.stats.DecaySpectrum({'Xe136': 1e-3}, volume=10.0)
|
|
assert d_stable.integral() == pytest.approx(0.0)
|
|
|
|
|
|
def test_decay_spectrum_clip(decay_spectrum_chain):
|
|
# Stable / non-emitting nuclides are removed unconditionally
|
|
d = openmc.stats.DecaySpectrum(
|
|
{'I135': 1e-3, 'Xe135': 5e-4, 'Xe136': 1.0, 'Cs135': 1.0},
|
|
volume=10.0,
|
|
)
|
|
d_clip = d.clip()
|
|
assert 'Xe136' not in d_clip.nuclides
|
|
assert 'Cs135' not in d_clip.nuclides
|
|
assert 'I135' in d_clip.nuclides
|
|
assert 'Xe135' in d_clip.nuclides
|
|
# Original is unchanged
|
|
assert 'Xe136' in d.nuclides
|
|
|
|
# inplace=True modifies and returns the same object
|
|
d_same = d.clip(inplace=True)
|
|
assert d_same is d
|
|
assert 'Xe136' not in d.nuclides
|
|
|
|
# A nuclide with negligible emission rate is removed by tolerance clipping.
|
|
# U235 has a very small integral (~4e-17 Bq/atom) compared with I135 (~4e-5)
|
|
d_tight = openmc.stats.DecaySpectrum(
|
|
{'I135': 1e-3, 'U235': 1e-3}, volume=10.0
|
|
)
|
|
d_tight_clip = d_tight.clip(tolerance=1e-9)
|
|
assert 'U235' not in d_tight_clip.nuclides
|
|
assert 'I135' in d_tight_clip.nuclides
|
|
|
|
# All non-emitters -> empty nuclides dict
|
|
d_empty = openmc.stats.DecaySpectrum({'Xe136': 1e-3}, volume=10.0)
|
|
d_empty.clip(inplace=True)
|
|
assert d_empty.nuclides == {}
|