def _stagger_sample(self, box, resolution):
"""
Samples this field on a staggered grid.
In addition to sampling, extrapolates the field using an occupancy mask generated from the points.
:param box: physical dimensions of the grid
:param resolution: grid resolution
:return: StaggeredGrid
"""
resolution = np.array(resolution)
valid_indices = math.to_int(math.floor(self.sample_points))
valid_indices = math.minimum(math.maximum(0, valid_indices), resolution - 1)
# Correct format for math.scatter
valid_indices = batch_indices(valid_indices)
active_mask = math.scatter(self.sample_points, valid_indices, 1, math.concat([[valid_indices.shape[0]], resolution, [1]], axis=-1), duplicates_handling='any')
mask = math.pad(active_mask, [[0, 0]] + [[1, 1]] * self.rank + [[0, 0]], "constant")
if isinstance(self.data, (int, float, np.ndarray)):
values = math.zeros_like(self.sample_points) + self.data
else:
values = self.data
result = []
ones_1d = math.unstack(math.ones_like(values), axis=-1)[0]
staggered_shape = [i + 1 for i in resolution]
dx = box.size / resolution
dims = range(len(resolution))
for d in dims:
staggered_offset = math.stack([(0.5 * dx[i] * ones_1d if i == d else 0.0 * ones_1d) for i in dims], axis=-1)
indices = math.to_int(math.floor(self.sample_points + staggered_offset))
valid_indices = math.maximum(0, math.minimum(indices, resolution))
valid_indices = batch_indices(valid_indices)
values_d = math.expand_dims(math.unstack(values, axis=-1)[d], axis=-1)
result.append(math.scatter(self.sample_points, valid_indices, values_d, [indices.shape[0]] + staggered_shape + [1], duplicates_handling=self.mode))
d_slice = tuple([(slice(0, -2) if i == d else slice(1,-1)) for i in dims])
u_slice = tuple([(slice(2, None) if i == d else slice(1,-1)) for i in dims])
active_mask = math.minimum(mask[(slice(None),) + d_slice + (slice(None),)], active_mask)
active_mask = math.minimum(mask[(slice(None),) + u_slice + (slice(None),)], active_mask)
staggered_tensor_prep = unstack_staggered_tensor(math.concat(result, axis=-1))
grid_values = StaggeredGrid(staggered_tensor_prep)
# Fix values at boundary of liquids (using StaggeredGrid these might not receive a value, so we replace it with a value inside the liquid)
grid_values, _ = extrapolate(grid_values, active_mask, voxel_distance=2)
return grid_values
def _grid_sample(self, box, resolution):
"""
Samples this field on a regular grid.
:param box: physical dimensions of the grid
:param resolution: grid resolution
:return: CenteredGrid
"""
valid_indices = math.to_int(math.floor(self.sample_points))
valid_indices = math.minimum(math.maximum(0, valid_indices), resolution - 1)
# Correct format for math.scatter
valid_indices = batch_indices(valid_indices)
scattered = math.scatter(self.sample_points, valid_indices, self.data, math.concat([[valid_indices.shape[0]], resolution, [1]], axis=-1), duplicates_handling=self.mode)
return CenteredGrid(data=scattered, box=box, extrapolation='constant', name=self.name+'_centered')