def apply(self, U, ind=None, mu=None):
assert isinstance(U, NumpyVectorArray)
assert U in self.source
mu = self.parse_parameter(mu)
if not hasattr(self, '_grid_data'):
self._fetch_grid_data()
ind = range(len(U)) if ind is None else ind
U = U.data
R = np.zeros((len(ind), self.source.dim))
bi = self.boundary_info
gd = self._grid_data
SUPE = gd['SUPE']
VOLS0 = gd['VOLS0']
VOLS1 = gd['VOLS1']
BOUNDARIES = gd['BOUNDARIES']
CENTERS = gd['CENTERS']
DIRICHLET_BOUNDARIES = gd['DIRICHLET_BOUNDARIES']
NEUMANN_BOUNDARIES = gd['NEUMANN_BOUNDARIES']
UNIT_OUTER_NORMALS = gd['UNIT_OUTER_NORMALS']
if bi.has_dirichlet:
if hasattr(self, '_dirichlet_values'):
dirichlet_values = self._dirichlet_values
elif self.dirichlet_data is not None:
dirichlet_values = self.dirichlet_data(
CENTERS[DIRICHLET_BOUNDARIES], mu=mu)
else:
dirichlet_values = np.zeros_like(DIRICHLET_BOUNDARIES)
F_dirichlet = self.numerical_flux.evaluate_stage1(
dirichlet_values, mu)
for i, j in enumerate(ind):
Ui = U[j]
Ri = R[i]
F = self.numerical_flux.evaluate_stage1(Ui, mu)
F_edge = [f[SUPE] for f in F]
for f in F_edge:
f[BOUNDARIES, 1] = f[BOUNDARIES, 0]
if bi.has_dirichlet:
for f, f_d in zip(F_edge, F_dirichlet):
f[DIRICHLET_BOUNDARIES, 1] = f_d
NUM_FLUX = self.numerical_flux.evaluate_stage2(
F_edge, UNIT_OUTER_NORMALS, VOLS1, mu)
if bi.has_neumann:
NUM_FLUX[NEUMANN_BOUNDARIES] = 0
iadd_masked(Ri, NUM_FLUX, SUPE[:, 0])
isub_masked(Ri, NUM_FLUX, SUPE[:, 1])
R /= VOLS0
return NumpyVectorArray(R)
def apply(self, U, ind=None, mu=None):
assert isinstance(U, NumpyVectorArray)
assert U in self.source
mu = self.parse_parameter(mu)
if not hasattr(self, '_grid_data'):
self._fetch_grid_data()
ind = range(len(U)) if ind is None else ind
U = U.data
R = np.zeros((len(ind), self.source.dim))
bi = self.boundary_info
gd = self._grid_data
SUPE = gd['SUPE']
VOLS0 = gd['VOLS0']
VOLS1 = gd['VOLS1']
BOUNDARIES = gd['BOUNDARIES']
CENTERS = gd['CENTERS']
DIRICHLET_BOUNDARIES = gd['DIRICHLET_BOUNDARIES']
NEUMANN_BOUNDARIES = gd['NEUMANN_BOUNDARIES']
UNIT_OUTER_NORMALS = gd['UNIT_OUTER_NORMALS']
if bi.has_dirichlet:
if hasattr(self, '_dirichlet_values'):
dirichlet_values = self._dirichlet_values
elif self.dirichlet_data is not None:
dirichlet_values = self.dirichlet_data(CENTERS[DIRICHLET_BOUNDARIES], mu=mu)
else:
dirichlet_values = np.zeros_like(DIRICHLET_BOUNDARIES)
F_dirichlet = self.numerical_flux.evaluate_stage1(dirichlet_values, mu)
for i, j in enumerate(ind):
Ui = U[j]
Ri = R[i]
F = self.numerical_flux.evaluate_stage1(Ui, mu)
F_edge = [f[SUPE] for f in F]
for f in F_edge:
f[BOUNDARIES, 1] = f[BOUNDARIES, 0]
if bi.has_dirichlet:
for f, f_d in zip(F_edge, F_dirichlet):
f[DIRICHLET_BOUNDARIES, 1] = f_d
NUM_FLUX = self.numerical_flux.evaluate_stage2(F_edge, UNIT_OUTER_NORMALS, VOLS1, mu)
if bi.has_neumann:
NUM_FLUX[NEUMANN_BOUNDARIES] = 0
iadd_masked(Ri, NUM_FLUX, SUPE[:, 0])
isub_masked(Ri, NUM_FLUX, SUPE[:, 1])
R /= VOLS0
return NumpyVectorArray(R)
def apply(self, U, mu=None):
assert U in self.source
mu = self.parse_parameter(mu)
if not hasattr(self, "_grid_data"):
self._fetch_grid_data()
U = U.data
R = np.zeros((len(U), self.source.dim))
bi = self.boundary_info
gd = self._grid_data
SUPE = gd["SUPE"]
VOLS0 = gd["VOLS0"]
VOLS1 = gd["VOLS1"]
BOUNDARIES = gd["BOUNDARIES"]
CENTERS = gd["CENTERS"]
DIRICHLET_BOUNDARIES = gd["DIRICHLET_BOUNDARIES"]
NEUMANN_BOUNDARIES = gd["NEUMANN_BOUNDARIES"]
UNIT_OUTER_NORMALS = gd["UNIT_OUTER_NORMALS"]
if bi.has_dirichlet:
if hasattr(self, "_dirichlet_values"):
dirichlet_values = self._dirichlet_values
elif self.dirichlet_data is not None:
dirichlet_values = self.dirichlet_data(CENTERS[DIRICHLET_BOUNDARIES], mu=mu)
else:
dirichlet_values = np.zeros_like(DIRICHLET_BOUNDARIES)
F_dirichlet = self.numerical_flux.evaluate_stage1(dirichlet_values, mu)
for i, j in enumerate(range(len(U))):
Ui = U[j]
Ri = R[i]
F = self.numerical_flux.evaluate_stage1(Ui, mu)
F_edge = [f[SUPE] for f in F]
for f in F_edge:
f[BOUNDARIES, 1] = f[BOUNDARIES, 0]
if bi.has_dirichlet:
for f, f_d in zip(F_edge, F_dirichlet):
f[DIRICHLET_BOUNDARIES, 1] = f_d
NUM_FLUX = self.numerical_flux.evaluate_stage2(F_edge, UNIT_OUTER_NORMALS, VOLS1, mu)
if bi.has_neumann:
NUM_FLUX[NEUMANN_BOUNDARIES] = 0
iadd_masked(Ri, NUM_FLUX, SUPE[:, 0])
isub_masked(Ri, NUM_FLUX, SUPE[:, 1])
R /= VOLS0
return self.range.make_array(R)
def apply(self, U, ind=None, mu=None):
assert isinstance(U, NumpyVectorArray)
assert U in self.source
mu = self.parse_parameter(mu)
ind = xrange(len(U)) if ind is None else ind
U = U.data
R = np.zeros((len(ind), self.source.dim))
g = self.grid
bi = self.boundary_info
SUPE = g.superentities(1, 0)
SUPI = g.superentity_indices(1, 0)
assert SUPE.ndim == 2
VOLS = g.volumes(1)
boundaries = g.boundaries(1)
unit_outer_normals = g.unit_outer_normals()[SUPE[:, 0], SUPI[:, 0]]
if bi.has_dirichlet:
dirichlet_boundaries = bi.dirichlet_boundaries(1)
if hasattr(self, '_dirichlet_values'):
dirichlet_values = self._dirichlet_values
elif self.dirichlet_data is not None:
dirichlet_values = self.dirichlet_data(g.centers(1)[dirichlet_boundaries], mu=mu)
else:
dirichlet_values = np.zeros_like(dirichlet_boundaries)
F_dirichlet = self.numerical_flux.evaluate_stage1(dirichlet_values, mu)
for i, j in enumerate(ind):
Ui = U[j]
Ri = R[i]
F = self.numerical_flux.evaluate_stage1(Ui, mu)
F_edge = [f[SUPE] for f in F]
for f in F_edge:
f[boundaries, 1] = f[boundaries, 0]
if bi.has_dirichlet:
for f, f_d in izip(F_edge, F_dirichlet):
f[dirichlet_boundaries, 1] = f_d
NUM_FLUX = self.numerical_flux.evaluate_stage2(F_edge, unit_outer_normals, VOLS, mu)
if bi.has_neumann:
NUM_FLUX[bi.neumann_boundaries(1)] = 0
iadd_masked(Ri, NUM_FLUX, SUPE[:, 0])
isub_masked(Ri, NUM_FLUX, SUPE[:, 1])
R /= g.volumes(0)
return NumpyVectorArray(R)
def apply(self, U, ind=None, mu=None):
assert isinstance(U, NumpyVectorArray)
assert U in self.source
mu = self.parse_parameter(mu)
ind = xrange(len(U)) if ind is None else ind
U = U.data
R = np.zeros((len(ind), self.source.dim))
g = self.grid
bi = self.boundary_info
SUPE = g.superentities(1, 0)
SUPI = g.superentity_indices(1, 0)
assert SUPE.ndim == 2
VOLS = g.volumes(1)
boundaries = g.boundaries(1)
unit_outer_normals = g.unit_outer_normals()[SUPE[:, 0], SUPI[:, 0]]
if bi.has_dirichlet:
dirichlet_boundaries = bi.dirichlet_boundaries(1)
if hasattr(self, '_dirichlet_values'):
dirichlet_values = self._dirichlet_values
elif self.dirichlet_data is not None:
dirichlet_values = self.dirichlet_data(g.centers(1)[dirichlet_boundaries], mu=mu)
else:
dirichlet_values = np.zeros_like(dirichlet_boundaries)
F_dirichlet = self.numerical_flux.evaluate_stage1(dirichlet_values, mu)
for i, j in enumerate(ind):
Ui = U[j]
Ri = R[i]
F = self.numerical_flux.evaluate_stage1(Ui, mu)
F_edge = [f[SUPE] for f in F]
for f in F_edge:
f[boundaries, 1] = f[boundaries, 0]
if bi.has_dirichlet:
for f, f_d in izip(F_edge, F_dirichlet):
f[dirichlet_boundaries, 1] = f_d
NUM_FLUX = self.numerical_flux.evaluate_stage2(F_edge, unit_outer_normals, VOLS, mu)
if bi.has_neumann:
NUM_FLUX[bi.neumann_boundaries(1)] = 0
iadd_masked(Ri, NUM_FLUX, SUPE[:, 0])
isub_masked(Ri, NUM_FLUX, SUPE[:, 1])
R /= g.volumes(0)
return NumpyVectorArray(R)
def apply(self, U, ind=None, mu=None):
assert isinstance(U, NumpyVectorArray)
assert U.dim == self.dim_source
ind = xrange(len(U)) if ind is None else ind
U = U._array
R = np.zeros((len(ind), self.dim_source))
grid = self.grid
N = grid.neighbours(0, 0)
SUPE = grid.superentities(1, 0)
SUPI = grid.superentity_indices(1, 0)
assert SUPE.ndim == 2
VOLS = grid.volumes(1)
for i, j in enumerate(ind):
Ui = U[j]
Ri = R[i]
F = self.flux(Ui, self.map_parameter(mu, 'flux'))
F_edge = F[SUPE]
F_edge[SUPE == -1] = 0
F_edge = np.sum(np.sum(F_edge, axis=1) * grid.unit_outer_normals()[SUPE[:,0], SUPI[:,0]], axis=1)
U_edge = Ui[SUPE]
U_edge[SUPE == -1] = 0
U_edge = (U_edge[:,0] - U_edge[:,1]) * (1. / self.lmbda)
TOT_edge = F_edge + U_edge
TOT_edge *= 0.5 * VOLS
# for k in xrange(len(TOT_edge)):
# Ri[SUPE[k,0]] += TOT_edge[k]
# Ri[SUPE[k,1]] -= TOT_edge[k]
# Ri[SUPE[:,0]] += TOT_edge
# Ri[SUPE[:,1]] -= TOT_edge
iadd_masked(Ri, TOT_edge, SUPE[:,0])
isub_masked(Ri, TOT_edge, SUPE[:,1])
R /= grid.volumes(0)
return NumpyVectorArray(R)
