yt.utilities.amr_kdtree.amr_kdtree module

class yt.utilities.amr_kdtree.amr_kdtree.AMRKDTree(ds, min_level=None, max_level=None, data_source=None)

Bases: ParallelAnalysisInterface

A KDTree for AMR data.

Not applicable to particle or octree-based datasets.

comm = None

count_cells()

count_volume()

fields = None

get_brick_data(node)

get_dependencies(fields)

get_node(nodeid)

get_node_arrays()

get_reduce_owners()

initialize_source(fields, log_fields, no_ghost)

join_parallel_trees()

load_kd_bricks(fn=None)

locate_neighbors(grid, ci)

Given a grid and cell index, finds the 26 neighbor grids and cell indices.

Parameters:

• grid (Grid Object) – Grid containing the cell of interest

• ci (array-like) – The cell index of the cell of interest

Returns:

• grids (Numpy array of Grid objects)

• cis (List of neighbor cell index tuples)

• Both of these are neighbors that, relative to the current cell

• index (i,j,k), are ordered as

• (i-1, j-1, k-1), (i-1, j-1, k ), (i-1, j-1, k+1), …

• (i-1, j , k-1), (i-1, j , k ), (i-1, j , k+1), …

• (i+1, j+1, k-1), (i-1, j-1, k ), (i+1, j+1, k+1)

• That is they start from the lower left and proceed to upper

• right varying the third index most frequently. Note that the

• center cell (i,j,k) is omitted.

locate_neighbors_from_position(position)

Given a position, finds the 26 neighbor grids and cell indices.

This is a mostly a wrapper for locate_neighbors.

Parameters:

position (array-like) – Position of interest

Returns:

• grids (Numpy array of Grid objects)

• cis (List of neighbor cell index tuples)

• Both of these are neighbors that, relative to the current cell

• index (i,j,k), are ordered as

• (i-1, j-1, k-1), (i-1, j-1, k ), (i-1, j-1, k+1), …

• (i-1, j , k-1), (i-1, j , k ), (i-1, j , k+1), …

• (i+1, j+1, k-1), (i-1, j-1, k ), (i+1, j+1, k+1)

• That is they start from the lower left and proceed to upper

• right varying the third index most frequently. Note that the

• center cell (i,j,k) is omitted.

locate_node(pos)

log_fields = None

no_ghost = True

partition_index_2d(axis)

partition_index_3d(ds, padding=0.0, rank_ratio=1)

partition_index_3d_bisection_list()

Returns an array that is used to drive _partition_index_3d_bisection, below.

partition_region_3d(left_edge, right_edge, padding=0.0, rank_ratio=1)

Given a region, it subdivides it into smaller regions for parallel analysis.

rebuild_tree_from_array(nids, pids, lids, rids, les, res, gids, splitdims, splitposs)

reduce_tree_images(image, viewpoint, *, use_opacity=True)

set_fields(fields, log_fields, no_ghost, force=False)

slice_traverse(viewpoint=None)

store_kd_bricks(fn=None)

traverse(viewpoint=None)

class yt.utilities.amr_kdtree.amr_kdtree.Tree(ds, comm_rank=0, comm_size=1, left=None, right=None, min_level=None, max_level=None, data_source=None)

Bases: object

add_grids(grids)

build()

check_tree()

sum_cells(all_cells=False)