def at(self, other_field):
if self.compatible(other_field):
return self
if isinstance(other_field, CenteredGrid) and np.allclose(
self.dx, other_field.dx):
paddings = _required_paddings_transposed(self.box, self.dx,
other_field.box)
if math.sum(paddings) == 0:
origin_in_local = self.box.global_to_local(
other_field.box.lower) * self.resolution
data = _crop_for_interpolation(self.data, origin_in_local,
other_field.resolution)
dimensions = self.resolution != other_field.resolution
dimensions = [
d for d in math.spatial_dimensions(data)
if dimensions[d - 1]
]
data = math.interpolate_linear(data, origin_in_local % 1.0,
dimensions)
return CenteredGrid(data,
other_field.box,
name=self.name,
batch_size=self._batch_size)
elif math.sum(paddings) < 16:
padded = self.padded(np.transpose(paddings).tolist())
return padded.at(other_field)
return Field.at(self, other_field)
def at(self,
other_field,
collapse_dimensions=True,
force_optimization=False,
return_self_if_compatible=False):
if self.compatible(
other_field
): # and return_self_if_compatible: not applicable for fields with Points
return self
if isinstance(other_field, CenteredGrid) and np.allclose(
self.dx, other_field.dx):
paddings = _required_paddings_transposed(self.box, self.dx,
other_field.box)
if math.sum(paddings) == 0:
origin_in_local = self.box.global_to_local(
other_field.box.lower) * self.resolution
data = _crop_for_interpolation(self.data, origin_in_local,
other_field.resolution)
dimensions = self.resolution != other_field.resolution
dimensions = [
d for d in math.spatial_dimensions(data)
if dimensions[d - 1]
]
data = math.interpolate_linear(data, origin_in_local % 1.0,
dimensions)
return CenteredGrid(data,
other_field.box,
name=self.name,
batch_size=self._batch_size)
elif math.sum(paddings) < 16:
padded = self.padded(np.transpose(paddings).tolist())
return padded.at(other_field, collapse_dimensions,
force_optimization)
return Field.at(self,
other_field,
force_optimization=force_optimization)