def div(self, shift=None):
space_dims = [d for d in self.dimensions if d.is_Space]
shift_x0 = make_shift_x0(shift, (len(space_dims), ))
return Add(*[
getattr(self, 'd%s' % d.name)(x0=shift_x0(shift, d, None, i))
for i, d in enumerate(space_dims)
])
def div(self, shift=None):
"""
Divergence of the VectorFunction, creates the divergence Function.
"""
shift_x0 = make_shift_x0(shift, (len(self.space_dimensions), ))
return sum([
getattr(self[i], 'd%s' % d.name)(x0=shift_x0(shift, d, None, i))
for i, d in enumerate(self.space_dimensions)
])
def grad(self, shift=None):
from devito.types.tensor import VectorFunction, VectorTimeFunction
space_dims = [d for d in self.dimensions if d.is_Space]
shift_x0 = make_shift_x0(shift, (len(space_dims),))
comps = [getattr(self, 'd%s' % d.name)(x0=shift_x0(shift, d, None, i))
for i, d in enumerate(space_dims)]
vec_func = VectorTimeFunction if self.is_TimeDependent else VectorFunction
return vec_func(name='grad_%s' % self.name, time_order=self.time_order,
space_order=self.space_order, components=comps, grid=self.grid)
def grad(self, shift=None):
"""
Gradient of the VectorFunction, creates the gradient TensorFunction.
"""
func = tens_func(self)
ndim = len(self.space_dimensions)
shift_x0 = make_shift_x0(shift, (ndim, ndim))
comps = [[
getattr(f, 'd%s' % d.name)(x0=shift_x0(shift, d, i, j))
for j, d in enumerate(self.space_dimensions)
] for i, f in enumerate(self)]
return func._new(comps)
def div(self, shift=None):
"""
Divergence of the TensorFunction (is a VectorFunction).
"""
comps = []
func = vec_func(self)
ndim = len(self.space_dimensions)
shift_x0 = make_shift_x0(shift, (ndim, ndim))
for i in range(len(self.space_dimensions)):
comps.append(
sum([
getattr(self[j, i],
'd%s' % d.name)(x0=shift_x0(shift, d, i, j))
for j, d in enumerate(self.space_dimensions)
]))
return func._new(comps)
