def grid(self,
value: Field or Tensor or Number or Geometry or callable = 0.,
type: type = CenteredGrid,
extrapolation: math.Extrapolation = None) -> CenteredGrid or StaggeredGrid:
"""
*Deprecated* due to inconsistent extrapolation selection. Use `scalar_grid()` or `vector_grid()` instead.
Creates a grid matching the resolution and bounds of the domain.
The grid is created from the given `value` which must be one of the following:
* Number (int, float, complex or zero-dimensional tensor): all grid values will be equal to `value`. This has a near-zero memory footprint.
* Field: the given value is resampled to the grid cells of this Domain.
* Tensor with spatial dimensions matching the domain resolution: grid values will equal `value`.
* Geometry: grid values are determined from the volume overlap between grid cells and geometry. Non-overlapping = 0, fully enclosed grid cell = 1.
* function(location: Tensor) returning one of the above.
Args:
value: constant, Field, Tensor or function specifying the grid values
type: type of Grid to create, must be either CenteredGrid or StaggeredGrid
extrapolation: (optional) grid extrapolation, defaults to Domain.boundaries['scalar_extrapolation']
Returns:
Grid of specified type
"""
warnings.warn("Domain.grid is deprecated. Use scalar_grid or vector_grid instead.", DeprecationWarning)
extrapolation = extrapolation or self.boundaries['scalar_extrapolation']
if type is CenteredGrid:
return CenteredGrid.sample(value, self.resolution, self.bounds, extrapolation)
elif type is StaggeredGrid:
return StaggeredGrid.sample(value, self.resolution, self.bounds, extrapolation)
else:
raise ValueError('Unknown grid type: %s' % type)
def vector_grid(
self,
value: Field or Tensor or Number or Geometry or callable = 0.,
type: type = CenteredGrid,
extrapolation: math.Extrapolation = None
) -> CenteredGrid or StaggeredGrid:
"""
Creates a vector grid matching the resolution and bounds of the domain.
The grid is created from the given `value` which must be one of the following:
* Number (int, float, complex or zero-dimensional tensor): all grid values will be equal to `value`. This has a near-zero memory footprint.
* Field: the given value is resampled to the grid cells of this Domain.
* Tensor with spatial dimensions matcing the domain resolution: grid values will equal `value`.
* Geometry: grid values are determined from the volume overlap between grid cells and geometry. Non-overlapping = 0, fully enclosed grid cell = 1.
* function(location: Tensor) returning one of the above.
The returned grid will have a vector dimension with size equal to the rank of the domain.
Args:
value: constant, Field, Tensor or function specifying the grid values
type: class of Grid to create, must be either CenteredGrid or StaggeredGrid
extrapolation: (optional) grid extrapolation, defaults to Domain.boundaries['vector_extrapolation']
Returns:
Grid of specified type
"""
extrapolation = extrapolation or self.boundaries['vector_extrapolation']
if type is CenteredGrid:
grid = CenteredGrid.sample(value, self.resolution, self.bounds,
extrapolation)
if grid.shape.channel.rank == 0:
grid = grid.with_(values=math.expand_channel(
grid.values, 'vector', dim_size=self.rank))
else:
assert grid.shape.channel.sizes[0] == self.rank
return grid
elif type is StaggeredGrid:
return StaggeredGrid.sample(value, self.resolution, self.bounds,
extrapolation)
else:
raise ValueError('Unknown grid type: %s' % type)
def scalar_grid(self,
value: Field or Tensor or Number or Geometry or callable = 0.,
extrapolation: math.Extrapolation = None) -> CenteredGrid:
"""
Creates a scalar grid matching the resolution and bounds of the domain.
The grid is created from the given `value` which must be one of the following:
* Number (int, float, complex or zero-dimensional tensor): all grid values will be equal to `value`. This has a near-zero memory footprint.
* Scalar `Field`: the given value is resampled to the grid cells of this Domain.
* Tensor with spatial dimensions matching the domain resolution: grid values will equal `value`.
* Geometry: grid values are determined from the volume overlap between grid cells and geometry. Non-overlapping = 0, fully enclosed grid cell = 1.
* function(location: Tensor) returning one of the above.
* Native tensor: the number and order of axes are matched with the resolution of the domain.
Args:
value: constant, Field, Tensor or function specifying the grid values
extrapolation: (optional) grid extrapolation, defaults to Domain.boundaries['scalar_extrapolation']
Returns:
`CenteredGrid` with no channel dimensions
"""
extrapolation = extrapolation or self.boundaries['scalar_extrapolation']
if isinstance(value, Field):
assert_same_rank(value.spatial_rank, self.rank, f"Cannot resample {value.spatial_rank}D field to {self.rank}D domain.")
elif isinstance(value, Tensor):
assert value.shape.channel.rank == 0
elif isinstance(value, (Number, Geometry)):
pass
elif callable(value):
pass
else:
try:
value = math.wrap(value, names=self.resolution.names)
except AssertionError:
pass
value = math.wrap(value)
result = CenteredGrid.sample(value, self.resolution, self.bounds, extrapolation)
assert result.shape.channel_rank == 0
return result