def _shift_resample(self, resolution, box, threshold=1e-5, max_padding=20):
lower = math.to_int(
math.ceil(
math.maximum(0, self.box.lower - box.lower) / self.dx -
threshold))
upper = math.to_int(
math.ceil(
math.maximum(0, box.upper - self.box.upper) / self.dx -
threshold))
total_padding = math.sum(lower) + math.sum(upper)
if total_padding > max_padding:
return NotImplemented
elif total_padding > 0:
from phi.field import pad
padded = pad(
self, {
dim: (int(lower[i]), int(upper[i]))
for i, dim in enumerate(self.shape.spatial.names)
})
grid_box, grid_resolution, grid_values = padded.box, padded.resolution, padded.values
else:
grid_box, grid_resolution, grid_values = self.box, self.resolution, self.values
origin_in_local = grid_box.global_to_local(box.lower) * grid_resolution
data = math.sample_subgrid(grid_values, origin_in_local, resolution)
return data
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 _crop_for_interpolation(data, offset_float, window_resolution):
offset = math.to_int(offset_float)
slices = [
slice(o, o + res + 1) for o, res in zip(offset, window_resolution)
]
data = data[tuple([slice(None)] + slices + [slice(None)])]
return data
def cell_index(self, global_position):
local_position = self.box.global_to_local(
global_position) * self.resolution
position = math.to_int(local_position - 0.5)
position = math.maximum(0, position)
position = math.minimum(position, self.resolution - 1)
return position
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
"""
sample_indices_nd = math.to_int(
math.round(box.global_to_local(self.sample_points) * resolution))
sample_indices_nd = math.minimum(
math.maximum(0, sample_indices_nd), resolution - 1
) # Snap outside points to edges, otherwise scatter raises an error
# Correct format for math.scatter
valid_indices = _batch_indices(sample_indices_nd)
shape = (math.shape(
self.data)[0], ) + tuple(resolution) + (self.data.shape[-1], )
scattered = math.scatter(self.sample_points,
valid_indices,
self.data,
shape,
duplicates_handling=self.mode)
return CenteredGrid(data=scattered,
box=box,
extrapolation='constant',
name=self.name + '_centered')
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')
def after_gather(self, selection: dict):
result = self
for name, selection in selection.items():
if isinstance(selection, int):
result = result.without(name)
elif isinstance(selection, slice):
start = selection.start or 0
stop = selection.stop or self.get_size(name)
step = selection.step or 1
if stop < 0:
stop += self.get_size(name)
assert stop >= 0
new_size = math.to_int(math.ceil(math.wrap((stop - start) / step)))
if new_size.rank == 0:
new_size = int(new_size) # NumPy array not allowed because not hashable
result = result.with_size(name, new_size)
else:
raise NotImplementedError(f"{type(selection)} not supported. Only (int, slice) allowed.")
return result
def _grid_scatter(self, box: Box, resolution: math.Shape):
"""
Approximately samples this field on a regular grid using math.scatter().
Args:
box: physical dimensions of the grid
resolution: grid resolution
box: Box:
resolution: math.Shape:
Returns:
CenteredGrid
"""
closest_index = math.to_int(math.round(box.global_to_local(self.points) * resolution - 0.5))
if self._add_overlapping:
duplicates_handling = 'add'
else:
duplicates_handling = 'mean'
scattered = math.scatter(closest_index, self.values, resolution, duplicates_handling=duplicates_handling, outside_handling='discard', scatter_dims=('points',))
return scattered
def _required_paddings_transposed(box, dx, target):
lower = math.to_int(
math.ceil(math.maximum(0, box.lower - target.lower) / dx))
upper = math.to_int(
math.ceil(math.maximum(0, target.upper - box.upper) / dx))
return [lower, upper]
