def _shift_resample(self,
resolution: Shape,
bounds: Box,
threshold=1e-5,
max_padding=20):
assert math.all_available(
bounds.lower, bounds.upper
), "Shift resampling requires 'bounds' to be available."
lower = math.to_int32(
math.ceil(
math.maximum(0, self.box.lower - bounds.lower) / self.dx -
threshold))
upper = math.to_int32(
math.ceil(
math.maximum(0, bounds.upper - self.box.upper) / self.dx -
threshold))
total_padding = (math.sum(lower) + math.sum(upper)).numpy()
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(
bounds.lower) * grid_resolution
data = math.sample_subgrid(grid_values, origin_in_local, resolution)
return data
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 extrapolation_helper(elements, t_shift, v_field, mask):
shift = math.ceil(math.max(
math.abs(elements.center - points.center))) - t_shift
t_shift += shift
v_field, mask = extrapolate_valid(v_field, mask, int(shift))
v_field *= accessible
return v_field, mask, t_shift
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 _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]