def test_bias_spatial():
"""This test tests a user-specified biasing scheme for which the only 1
bias group is supplied for a source distribution containing two energy
groups. This bias group is applied to both energy groups. In this test,
the user-supplied bias distribution that was choosen, correspondes to
uniform sampling, so that results can be checked against Case 1 in the
theory manual.
"""
seed(1953)
m = Mesh(structured=True,
structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]],
mats=None)
m.src = IMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
m.bias = IMeshTag(1, float)
m.bias[:] = [1, 1]
e_bounds = np.array([0, 0.5, 1.0])
m.mesh.save("sampling_mesh.h5m")
sampler = Sampler("sampling_mesh.h5m", "src", e_bounds, "bias")
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
num_e = 2
spatial_tally = np.zeros(shape=(num_divs, num_divs, num_divs))
e_tally = np.zeros(shape=(4)) # number of phase space groups
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s[0] < 3.0:
assert_almost_equal(s[4], 0.7) # hand calcs
else:
assert_almost_equal(s[4], 2.8) # hand calcs
spatial_tally[int(s[0] * num_divs / 3.5),
int(s[1] * num_divs / 1.0),
int(s[2] * num_divs / 1.0)] += score
if s[0] < 3 and s[3] < 0.5:
e_tally[0] += score
elif s[0] < 3 and s[3] > 0.5:
e_tally[1] += score
if s[0] > 3 and s[3] < 0.5:
e_tally[2] += score
if s[0] > 3 and s[3] > 0.5:
e_tally[3] += score
for i in range(0, 3):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(spatial_tally, i)[j, :, :])
assert (abs(halfspace_sum - 0.5) / 0.5 < 0.1)
expected_e_tally = [4. / 7, 2. / 7, 3. / 28, 1. / 28] # hand calcs
for i in range(4):
assert (abs(e_tally[i] - expected_e_tally[i]) / expected_e_tally[i] <
0.1)
def test_bias_spatial():
"""This test tests a user-specified biasing scheme for which the only 1
bias group is supplied for a source distribution containing two energy
groups. This bias group is applied to both energy groups. In this test,
the user-supplied bias distribution that was choosen, correspondes to
uniform sampling, so that results can be checked against Case 1 in the
theory manual.
"""
seed(1953)
m = Mesh(structured=True, structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]], mats=None)
m.src = IMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
m.bias = IMeshTag(1, float)
m.bias[:] = [1, 1]
e_bounds = np.array([0, 0.5, 1.0])
m.mesh.save("sampling_mesh.h5m")
sampler = Sampler("sampling_mesh.h5m", "src", e_bounds, "bias")
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
num_e = 2
spatial_tally = np.zeros(shape=(num_divs, num_divs, num_divs))
e_tally = np.zeros(shape=(4)) # number of phase space groups
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s[0] < 3.0:
assert_almost_equal(s[4], 0.7) # hand calcs
else:
assert_almost_equal(s[4], 2.8) # hand calcs
spatial_tally[int(s[0] * num_divs / 3.5), int(s[1] * num_divs / 1.0), int(s[2] * num_divs / 1.0)] += score
if s[0] < 3 and s[3] < 0.5:
e_tally[0] += score
elif s[0] < 3 and s[3] > 0.5:
e_tally[1] += score
if s[0] > 3 and s[3] < 0.5:
e_tally[2] += score
if s[0] > 3 and s[3] > 0.5:
e_tally[3] += score
for i in range(0, 3):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(spatial_tally, i)[j, :, :])
assert abs(halfspace_sum - 0.5) / 0.5 < 0.1
expected_e_tally = [4.0 / 7, 2.0 / 7, 3.0 / 28, 1.0 / 28] # hand calcs
for i in range(4):
assert abs(e_tally[i] - expected_e_tally[i]) / expected_e_tally[i] < 0.1
def test_bias():
"""This test tests that a user-specified biasing scheme:
1. Samples space uniformly according to the scheme.
2. Adjusts weights accordingly. Sample calculations are provided in Case 2
in the Theory Manual.
"""
seed(1953)
m = Mesh(structured=True,
structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]],
mats=None)
m.src = IMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
e_bounds = np.array([0, 0.5, 1.0])
m.bias = IMeshTag(2, float)
m.bias[:] = [[1.0, 2.0], [3.0, 3.0]]
m.mesh.save("sampling_mesh.h5m")
sampler = Sampler("sampling_mesh.h5m", "src", e_bounds, "bias")
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(4))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s[0] < 3:
if s[3] < 0.5:
assert_almost_equal(s[4], 1.6) # hand calcs
tally[0] += score
else:
assert_almost_equal(s[4], 0.4) # hand calcs
tally[1] += score
else:
if s[3] < 0.5:
assert_almost_equal(s[4], 2.4) # hand calcs
tally[2] += score
else:
assert_almost_equal(s[4], 0.8) # hand calcs
tally[3] += score
expected_tally = [0.25, 0.5, 0.125, 0.125] # hand calcs
for a, b in zip(tally, expected_tally):
assert (abs(a - b) / b < 0.25)
def test_bias():
"""This test tests that a user-specified biasing scheme:
1. Samples space uniformly according to the scheme.
2. Adjusts weights accordingly. Sample calculations are provided in Case 2
in the Theory Manual.
"""
seed(1953)
m = Mesh(structured=True, structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]], mats=None)
m.src = IMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
e_bounds = np.array([0, 0.5, 1.0])
m.bias = IMeshTag(2, float)
m.bias[:] = [[1.0, 2.0], [3.0, 3.0]]
m.mesh.save("sampling_mesh.h5m")
sampler = Sampler("sampling_mesh.h5m", "src", e_bounds, "bias")
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(4))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s[0] < 3:
if s[3] < 0.5:
assert_almost_equal(s[4], 1.6) # hand calcs
tally[0] += score
else:
assert_almost_equal(s[4], 0.4) # hand calcs
tally[1] += score
else:
if s[3] < 0.5:
assert_almost_equal(s[4], 2.4) # hand calcs
tally[2] += score
else:
assert_almost_equal(s[4], 0.8) # hand calcs
tally[3] += score
expected_tally = [0.25, 0.5, 0.125, 0.125] # hand calcs
for a, b in zip(tally, expected_tally):
assert abs(a - b) / b < 0.25
