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warp.fem.AdaptiveNanogrid#

class warp.fem.AdaptiveNanogrid(
cell_grid,
cell_level,
level_count,
temporary_store,
scalar_type=warp.float32,
cell_env=None,
env_offsets=None,
)[source]#

Adaptive sparse grid.

Parameters:
__init__(
cell_grid,
cell_level,
level_count,
temporary_store,
scalar_type=warp.float32,
cell_env=None,
env_offsets=None,
)[source]#

Construct an adaptive sparse grid geometry from an in-memory NanoVDB volume and a list of levels.

It is not recommended to use this constructor directly; see the helper functions warp.fem.adaptive_nanogrid_from_field() and warp.fem.adaptive_nanogrid_from_hierarchy().

Parameters:

  • cell_grid (Volume) – A warp volume (ideally backed by an index grid) whose voxels coordinates correspond to the lowest fine-resolution voxel of each cell. The cell’s extent is then given by the cell_level array. For instance, a voxel at coordinates ijk and level 0 corresponds to a fine cell at the same coordinates, a voxel at coordinates 2*ijk and level 1 corresponds to a cell spanning 2^3 voxels from 2*ijk to 2*ijk + (1,1,1), etc.

  • cell_level (array) – Refinement level for each voxel of the volume. Level 0 is the finest, level level_count-1 is the coarsest.

  • level_count (int) – Number of levels in the grid

  • scalar_type (type) – Scalar type for coordinate and weight computations (warp.float32 or warp.float64)

  • temporary_store (TemporaryStore)

  • cell_env (array | None)

  • env_offsets (array | None)

Methods

__init__(cell_grid, cell_level, level_count, ...)

Construct an adaptive sparse grid geometry from an in-memory NanoVDB volume and a list of levels.

boundary_side_count()

build_bvh([device])

Rebuild the geometry's Bounding Volume Hierarchy (BVH) for device from scratch.

bvh_id(device)

Return the BVH identifier for the given device, or 0 if unavailable.

cell_arg_value(device)

cell_bvh_groups(device)

Return optional per-cell BVH group indices on device.

cell_count()

cell_env_arg_value(device)

Return per-cell environment indices for geometry argument structs.

environment_count()

fill_cell_arg(arg, device)

fill_side_arg(arg, device)

fill_side_index_arg(arg, device)

from_environment_voxels(cell_ijks, ...[, ...])

Construct an adaptive sparse grid from per-environment active cells and levels.

make_filtered_cell_lookup([filter_func])

reference_cell()

reference_side()

side_arg_value(device)

side_count()

side_deformation_gradient(args, s)

Device function returning the gradient of world position with respect to reference side

side_index_arg_value(device)

side_normal(args, s)

Device function returning the element normal at a sample point

stacked_edge_count()

stacked_face_count()

supports_cell_lookup(device)

update_bvh([device])

Refit the geometry's BVH if it exists on device, or build it from scratch otherwise.

vertex_count()

Attributes

FACE_AXIS_MASK

FACE_INNER_OFFSET_BIT

FACE_OUTER_OFFSET_BIT

GRID_AXIS_FLAG

SideIndexArg

Structure containing arguments to be passed to device functions for indexing sides.

base

Return the base geometry from which this geometry derives its topology.

boundary_side_index

cell_closest_point

cell_coordinates

cell_deformation_gradient

cell_dimension

Manifold dimension of the geometry cells

cell_env

cell_environment_index

cell_grid

cell_inverse_deformation_gradient

cell_lookup

Device function for looking up the closest cell to a position.

cell_measure

cell_measure_ratio

cell_normal

cell_position

coarse_ijk

compute_cell_bvh_groups

coords_type

Warp vector type for element coordinates matching this geometry's scalar precision.

dimension

Dimension of the embedding space.

encode_axis_and_level

env_offsets

face_grid

find_cell

fine_ijk

name

Name of the geometry, including environment and scalar type suffixes.

sample_type

Warp struct type for samples matching this geometry's scalar precision.

scalar_type

side_closest_point

side_coordinates

side_environment_index

side_from_cell_coords

side_inner_cell_coords

side_inner_cell_index

side_inner_inverse_deformation_gradient

side_measure

side_measure_ratio

side_outer_cell_coords

side_outer_cell_index

side_outer_inverse_deformation_gradient

side_position

side_to_cell_arg

stacked_edge_grid

stacked_face_grid

transform

Transform matrix mapping index to world space.

vertex_grid

CellArg

Structure containing arguments to be passed to device functions evaluating cell-related quantities.

SideArg

Structure containing arguments to be passed to device functions evaluating side-related quantities.
dimension: int = 3#

Dimension of the embedding space.

classmethod from_environment_voxels(
cell_ijks,
cell_levels,
level_count,
env_offsets=None,
*,
voxel_size=1.0,
translation=(0.0, 0.0, 0.0),
transform=None,
temporary_store=None,
scalar_type=warp.float32,
device=None,
)[source]#

Construct an adaptive sparse grid from per-environment active cells and levels.

Coordinates and levels are interpreted in environment-local fine-grid index space. The helper packs all environments into one NanoVDB index grid and returns a geometry whose FEM world coordinates remain local to each environment.

Parameters:

  • cell_ijks (Sequence[array]) – Sequence of warp.vec3i arrays, one array per environment.

  • cell_levels (Sequence[array]) – Sequence of warp.uint8 arrays matching cell_ijks.

  • level_count (int) – Number of refinement levels in the grid.

  • env_offsets (array | Sequence[Sequence[int]] | None) – Optional packed-grid offsets, one warp.vec3i per environment. Offsets must be aligned to the coarsest cell size. If omitted, aligned offsets are generated along the x axis. Custom offsets are an advanced override for callers that need deterministic packed NanoVDB coordinates, for example to match an externally built volume. They must still keep active cells from different environments from sharing packed-grid faces.

  • voxel_size (int | float | Sequence[float] | None) – Fine-grid voxel size for the packed NanoVDB volume. Ignored if transform is provided.

  • translation – Translation between packed index and world spaces.

  • transform – Linear transform between packed index and world spaces.

  • temporary_store (TemporaryStore | None) – Shared pool from which to allocate temporary arrays.

  • scalar_type (type) – Scalar type for grid coordinates (warp.float32 or warp.float64).

  • device – CUDA device on which to build the packed volume.

property stacked_face_grid: Volume[source]#
stacked_face_count()[source]#
property stacked_edge_grid: Volume[source]#
stacked_edge_count()[source]#
fill_cell_arg(arg, device)[source]#
fill_side_arg(arg, device)[source]#
supports_cell_lookup(device)[source]#
cell_position = <Function AdaptiveNanogrid__cell_position(args: Any, s: Any)>#
Parameters:
cell_deformation_gradient = <Function AdaptiveNanogrid__cell_deformation_gradient(args: Any, s: Any)>#
Parameters:
cell_inverse_deformation_gradient = <Function AdaptiveNanogrid__cell_inverse_deformation_gradient(args: Any, s: Any)>#
Parameters:
cell_measure = <Function AdaptiveNanogrid__cell_measure(args: Any, s: Any)>#
Parameters:
cell_normal = <Function AdaptiveNanogrid__cell_normal(args: Any, s: Any)>#
Parameters:
cell_coordinates = <Function AdaptiveNanogrid__cell_coordinates(args: Any, cell_index: int, pos: Any)>#
Parameters:
cell_closest_point = <Function AdaptiveNanogrid__cell_closest_point(args: Any, cell_index: int, pos: Any)>#
Parameters:
side_position = <Function AdaptiveNanogrid__side_position(args: Any, s: Any)>#
Parameters:
side_inner_inverse_deformation_gradient = <Function AdaptiveNanogrid__side_inner_inverse_deformation_gradient(args: Any, s: Any)>#
Parameters:
side_outer_inverse_deformation_gradient = <Function AdaptiveNanogrid__side_outer_inverse_deformation_gradient(args: Any, s: Any)>#
Parameters:
side_measure = <Function AdaptiveNanogrid__side_measure(args: Any, s: Any)>#
Parameters:
side_measure_ratio = <Function AdaptiveNanogrid__side_measure_ratio(args: Any, s: Any)>#
Parameters:
side_inner_cell_index = <Function AdaptiveNanogrid__side_inner_cell_index(args: Any, side_index: int)>#
Parameters:
side_outer_cell_index = <Function AdaptiveNanogrid__side_outer_cell_index(args: Any, side_index: int)>#
Parameters:
side_inner_cell_coords = <Function AdaptiveNanogrid__side_inner_cell_coords(args: Any, side_index: int, side_coords: Any)>#
Parameters:

  • args (Any)

  • side_index (int)

  • side_coords (Any)

side_outer_cell_coords = <Function AdaptiveNanogrid__side_outer_cell_coords(args: Any, side_index: int, side_coords: Any)>#
Parameters:

  • args (Any)

  • side_index (int)

  • side_coords (Any)

side_from_cell_coords = <Function AdaptiveNanogrid__side_from_cell_coords(args: Any, side_index: int, element_index: int, element_coords: Any)>#
Parameters:

  • args (Any)

  • side_index (int)

  • element_index (int)

  • element_coords (Any)

side_to_cell_arg = <Function AdaptiveNanogrid__side_to_cell_arg(side_arg: Any)>#
Parameters:

side_arg (Any)

side_coordinates = <Function AdaptiveNanogrid__side_coordinates(args: Any, side_index: int, pos: Any)>#
Parameters:
side_closest_point = <Function AdaptiveNanogrid__side_closest_point(args: Any, side_index: int, pos: Any)>#
Parameters:
coarse_ijk = <Function AdaptiveNanogrid__coarse_ijk(ijk: vec3i, level: int)>#
Parameters:
fine_ijk = <Function AdaptiveNanogrid__fine_ijk(ijk: vec3i, level: int)>#
Parameters:
encode_axis_and_level = <Function AdaptiveNanogrid__encode_axis_and_level(ijk: vec3i, axis: int, level: int)>#
Parameters:
find_cell = <Function AdaptiveNanogrid__find_cell(cell_grid: uint64, ijk: vec3i, level_count: int, cell_level: array(ndim=1, dtype=uint8))>#
Parameters: