def __setitem__(self, key, value):
"""Key setting via custom cache functionality."""
# Set the E_g
if (key == 'E_g'):
value = np.array(value, dtype=float)
# If the E_gs are the same, don't set anything
if ('E_g' in self):
if (len(value) == len(self['E_g'])) and (value == self['E_g']).all():
return
# Otherwise, preload some stuff.
self._cache['partial_energy_matrix'] = partial_energy_matrix(value, self['E_n'])
# And remove any previous paramters dependent on E_g
dirty_keys = [k for k in self._cache if is_g_indexed(k)]
for dk in dirty_keys:
del self._cache[dk]
# Set the E_n
if (key == 'E_n'):
value = np.array(value, dtype=float)
# If the E_gs are the same, don't set anything
if ('E_n' in self):
if (len(value) == len(self['E_n'])) and (value == self['E_n']).all():
return
# Otherwise, preload some stuff.
if 'E_g' in self:
self._cache['partial_energy_matrix'] = partial_energy_matrix(self['E_g'], value)
# Set the high resolution flux, phi_n
if key == 'phi_n':
value = np.array(value, dtype=float)
# If the flux is same, don't set or remove anything
if ('phi_n' in self):
if (len(value) == len(self['phi_n'])) and (value == self['phi_n']).all():
return
# And remove any previous paramters dependent on phi_n
dirty_keys = [k for k in self._cache if (is_g_indexed(k) and k != 'E_g')]
for dk in dirty_keys:
del self._cache[dk]
# Set the value normally
self._cache[key] = value
def test_partial_energy_matrix2():
E_g = np.array([8.0, 4.0, 0.0])
E_n = np.array([10.0, 7.5, 5.0, 2.5, 0.0])
pem = partial_energy_matrix(E_g, E_n)
expected = np.array([[0.2, 1.0, 0.4, 0.0], [0.0, 0.0, 0.6, 1.0]])
assert_array_equal(pem, expected)
def dst_group_struct(self, dst_group_struct):
if dst_group_struct is None:
self._dst_group_struct = None
self._dst_ngroups = 0
self._src_to_dst_matrix = None
else:
self._dst_group_struct = np.asarray(dst_group_struct)
self._dst_ngroups = len(dst_group_struct) - 1
self._src_to_dst_matrix = partial_energy_matrix(dst_group_struct,
self._src_group_struct)
def test_partial_energy_matrix1():
# tests dispach to inc
E_g = np.array([0.0, 4.0, 8.0])
E_n = np.array([0.0, 2.5, 5.0, 7.5, 10.0])
pem = partial_energy_matrix(E_g, E_n)
expected = np.array([[1.0, 0.6, 0.0, 0.0], [0.0, 0.4, 1.0, 0.2]])
assert_array_equal(pem, expected)
def test_partial_energy_matrix2():
E_g = np.array([8.0, 4.0, 0.0])
E_n = np.array([10.0, 7.5, 5.0, 2.5, 0.0])
pem = partial_energy_matrix(E_g, E_n)
expected = np.array([[0.2, 1.0, 0.4, 0.0],
[0.0, 0.0, 0.6, 1.0]])
assert_array_equal(pem, expected)
def test_partial_energy_matrix1():
# tests dispach to inc
E_g = np.array([0.0, 4.0, 8.0])
E_n = np.array([0.0, 2.5, 5.0, 7.5, 10.0])
pem = partial_energy_matrix(E_g, E_n)
expected = np.array([[1.0, 0.6, 0.0, 0.0],
[0.0, 0.4, 1.0, 0.2]])
assert_array_equal(pem, expected)
def test_group_collapse1():
E_g = np.array([0.0, 4.0, 8.0])
E_n = np.array([0.0, 2.5, 5.0, 7.5, 10.0])
phi_n = np.array([0.0, 2.0, 1.0, 0.5])
sigma_n = np.array([1.0, 2.0, 3.0, 4.0])
expected = np.array([2.0, 5.0 / 1.9])
# First way of calling
observed = group_collapse(sigma_n, phi_n, E_g=E_g, E_n=E_n)
assert_array_almost_equal(observed, expected)
# Second method of calling
p_g = phi_g(E_g, E_n, phi_n)
pem = partial_energy_matrix(E_g, E_n)
observed = group_collapse(sigma_n, phi_n, phi_g=p_g, partial_energies=pem)
assert_array_almost_equal(observed, expected)
# bad call
assert_raises(ValueError, group_collapse, sigma_n, phi_n)
def test_group_collapse1():
E_g = np.array([0.0, 4.0, 8.0])
E_n = np.array([0.0, 2.5, 5.0, 7.5, 10.0])
phi_n = np.array([0.0, 2.0, 1.0, 0.5])
sigma_n = np.array([1.0, 2.0, 3.0, 4.0])
expected = np.array([2.0, 5.0 / 1.9])
# First way of calling
observed = group_collapse(sigma_n, phi_n, E_g=E_g, E_n=E_n)
assert_array_almost_equal(observed, expected)
# Second method of calling
p_g = phi_g(E_g, E_n, phi_n)
pem = partial_energy_matrix(E_g, E_n)
observed = group_collapse(sigma_n, phi_n, phi_g=p_g, partial_energies=pem)
assert_array_almost_equal(observed, expected)
# bad call
assert_raises(ValueError, group_collapse, sigma_n, phi_n)
