pykdtree-0.2/ 0000755 0007675 0007675 00000000000 12153111634 012623 5 ustar esn esn 0000000 0000000 pykdtree-0.2/pykdtree/ 0000755 0007675 0007675 00000000000 12153111634 014452 5 ustar esn esn 0000000 0000000 pykdtree-0.2/pykdtree/__init__.py 0000644 0007675 0007675 00000000000 12072551074 016557 0 ustar esn esn 0000000 0000000 pykdtree-0.2/pykdtree/_kdtree_core.c 0000644 0007675 0007675 00000122533 12152037506 017255 0 ustar esn esn 0000000 0000000 /*
pykdtree, Fast kd-tree implementation with OpenMP-enabled queries
Copyright (C) 2013 Esben S. Nielsen
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
*/
/*
This kd-tree implementation is based on the scipy.spatial.cKDTree by
Anne M. Archibald and libANN by David M. Mount and Sunil Arya.
*/
#include
#include
#include
#include
#define PA(i,d) (pa[no_dims * pidx[i] + d])
#define PASWAP(a,b) { uint32_t tmp = pidx[a]; pidx[a] = pidx[b]; pidx[b] = tmp; }
typedef struct
{
float cut_val;
int8_t cut_dim;
uint32_t start_idx;
uint32_t n;
float cut_bounds_lv;
float cut_bounds_hv;
struct Node_float *left_child;
struct Node_float *right_child;
} Node_float;
typedef struct
{
float *bbox;
int8_t no_dims;
uint32_t *pidx;
struct Node_float *root;
} Tree_float;
typedef struct
{
double cut_val;
int8_t cut_dim;
uint32_t start_idx;
uint32_t n;
double cut_bounds_lv;
double cut_bounds_hv;
struct Node_double *left_child;
struct Node_double *right_child;
} Node_double;
typedef struct
{
double *bbox;
int8_t no_dims;
uint32_t *pidx;
struct Node_double *root;
} Tree_double;
void insert_point_float(uint32_t *closest_idx, float *closest_dist, uint32_t pidx, float cur_dist, uint32_t k);
void get_bounding_box_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, float *bbox);
int partition_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, float *bbox, int8_t *cut_dim,
float *cut_val, uint32_t *n_lo);
Tree_float* construct_tree_float(float *pa, int8_t no_dims, uint32_t n, uint32_t bsp);
Node_float* construct_subtree_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, float *bbox);
Node_float * create_node_float(uint32_t start_idx, uint32_t n, int is_leaf);
void delete_subtree_float(Node_float *root);
void delete_tree_float(Tree_float *tree);
void print_tree_float(Node_float *root, int level);
float calc_dist_float(float *point1_coord, float *point2_coord, int8_t no_dims);
float get_cube_offset_float(int8_t dim, float *point_coord, float *bbox);
float get_min_dist_float(float *point_coord, int8_t no_dims, float *bbox);
void search_leaf_float(float *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, float *restrict point_coord,
uint32_t k, uint32_t *restrict closest_idx, float *restrict closest_dist);
void search_splitnode_float(Node_float *root, float *pa, uint32_t *pidx, int8_t no_dims, float *point_coord,
float min_dist, uint32_t k, float distance_upper_bound, float eps_fac, uint32_t * closest_idx, float *closest_dist);
void search_tree_float(Tree_float *tree, float *pa, float *point_coords,
uint32_t num_points, uint32_t k, float distance_upper_bound,
float eps, uint32_t *closest_idxs, float *closest_dists);
void insert_point_double(uint32_t *closest_idx, double *closest_dist, uint32_t pidx, double cur_dist, uint32_t k);
void get_bounding_box_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, double *bbox);
int partition_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, double *bbox, int8_t *cut_dim,
double *cut_val, uint32_t *n_lo);
Tree_double* construct_tree_double(double *pa, int8_t no_dims, uint32_t n, uint32_t bsp);
Node_double* construct_subtree_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, double *bbox);
Node_double * create_node_double(uint32_t start_idx, uint32_t n, int is_leaf);
void delete_subtree_double(Node_double *root);
void delete_tree_double(Tree_double *tree);
void print_tree_double(Node_double *root, int level);
double calc_dist_double(double *point1_coord, double *point2_coord, int8_t no_dims);
double get_cube_offset_double(int8_t dim, double *point_coord, double *bbox);
double get_min_dist_double(double *point_coord, int8_t no_dims, double *bbox);
void search_leaf_double(double *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, double *restrict point_coord,
uint32_t k, uint32_t *restrict closest_idx, double *restrict closest_dist);
void search_splitnode_double(Node_double *root, double *pa, uint32_t *pidx, int8_t no_dims, double *point_coord,
double min_dist, uint32_t k, double distance_upper_bound, double eps_fac, uint32_t * closest_idx, double *closest_dist);
void search_tree_double(Tree_double *tree, double *pa, double *point_coords,
uint32_t num_points, uint32_t k, double distance_upper_bound,
double eps, uint32_t *closest_idxs, double *closest_dists);
/************************************************
Insert point into priority queue
Params:
closest_idx : index queue
closest_dist : distance queue
pidx : permutation index of data points
cur_dist : distance to point inserted
k : number of neighbours
************************************************/
void insert_point_float(uint32_t *closest_idx, float *closest_dist, uint32_t pidx, float cur_dist, uint32_t k)
{
int i;
for (i = k - 1; i > 0; i--)
{
if (closest_dist[i - 1] > cur_dist)
{
closest_dist[i] = closest_dist[i - 1];
closest_idx[i] = closest_idx[i - 1];
}
else
{
break;
}
}
closest_idx[i] = pidx;
closest_dist[i] = cur_dist;
}
/************************************************
Get the bounding box of a set of points
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
n : number of points
bbox : bounding box (return)
************************************************/
void get_bounding_box_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, float *bbox)
{
float cur;
int8_t bbox_idx;
/* Use first data point to initialize */
for (int8_t i = 0; i < no_dims; i++)
{
bbox[2 * i] = bbox[2 * i + 1] = PA(0, i);
}
/* Update using rest of data points */
for (uint32_t i = 1; i < n; i++)
{
for (int8_t j = 0; j < no_dims; j++)
{
bbox_idx = 2 * j;
cur = PA(i, j);
if (cur < bbox[bbox_idx])
{
bbox[bbox_idx] = cur;
}
else if (cur > bbox[bbox_idx + 1])
{
bbox[bbox_idx + 1] = cur;
}
}
}
}
/************************************************
Partition a range of data points by manipulation the permutation index.
The sliding midpoint rule is used for the partitioning.
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
start_idx : index of first data point to use
n : number of data points
bbox : bounding box of data points
cut_dim : dimension used for partition (return)
cut_val : value of cutting point (return)
n_lo : number of point below cutting plane (return)
************************************************/
int partition_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, float *bbox, int8_t *cut_dim, float *cut_val, uint32_t *n_lo)
{
int8_t dim = 0;
uint32_t p, q;
float size = 0, min_val, max_val, split, side_len, cur_val;
uint32_t end_idx = start_idx + n - 1;
/* Find largest bounding box side */
for (int8_t i = 0; i < no_dims; i++)
{
side_len = bbox[2 * i + 1] - bbox[2 * i];
if (side_len > size)
{
dim = i;
size = side_len;
}
}
min_val = bbox[2 * dim];
max_val = bbox[2 * dim + 1];
/* Check for zero length or inconsistent */
if (min_val >= max_val)
return 1;
/* Use middle for splitting */
split = (min_val + max_val) / 2;
/* Partition all data points around middle */
p = start_idx;
q = end_idx;
while (p <= q)
{
if (PA(p, dim) < split)
{
p++;
}
else if (PA(q, dim) >= split)
{
/* Guard for underflow */
if (q > 0)
{
q--;
}
else
{
break;
}
}
else
{
PASWAP(p, q);
p++;
q--;
}
}
/* Check for empty splits */
if (p == start_idx)
{
/* No points less than split.
Split at lowest point instead.
Minimum 1 point will be in lower box.
*/
uint32_t j = start_idx;
split = PA(j, dim);
for (uint32_t i = start_idx + 1; i <= end_idx; i++)
{
/* Find lowest point */
cur_val = PA(i, dim);
if (cur_val < split)
{
j = i;
split = cur_val;
}
}
PASWAP(j, start_idx);
p = start_idx + 1;
}
else if (p == end_idx + 1)
{
/* No points greater than split.
Split at highest point instead.
Minimum 1 point will be in higher box.
*/
uint32_t j = end_idx;
split = PA(j, dim);
for (uint32_t i = start_idx; i < end_idx; i++)
{
/* Find highest point */
cur_val = PA(i, dim);
if (cur_val > split)
{
j = i;
split = cur_val;
}
}
PASWAP(j, end_idx);
p = end_idx;
}
/* Set return values */
*cut_dim = dim;
*cut_val = split;
*n_lo = p - start_idx;
return 0;
}
/************************************************
Construct a sub tree over a range of data points.
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
start_idx : index of first data point to use
n : number of data points
bsp : number of points per leaf
bbox : bounding box of set of data points
************************************************/
Node_float* construct_subtree_float(float *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, float *bbox)
{
/* Create new node */
int is_leaf = (n <= bsp);
Node_float *root = create_node_float(start_idx, n, is_leaf);
if (is_leaf)
{
/* Make leaf node */
root->cut_dim = -1;
}
else
{
/* Make split node */
int rval;
int8_t cut_dim;
uint32_t n_lo;
float cut_val;
/* Partition data set and set node info */
rval = partition_float(pa, pidx, no_dims, start_idx, n, bbox, &cut_dim, &cut_val, &n_lo);
if (rval == 1)
{
root->cut_dim = -1;
return root;
}
root->cut_val = cut_val;
root->cut_dim = cut_dim;
/* Recurse on both subsets */
float lv = bbox[2 * cut_dim];
float hv = bbox[2 * cut_dim + 1];
/* Set bounds for cut dimension */
root->cut_bounds_lv = lv;
root->cut_bounds_hv = hv;
/* Update bounding box before call to lower subset and restore after */
bbox[2 * cut_dim + 1] = cut_val;
root->left_child = (struct Node_float *)construct_subtree_float(pa, pidx, no_dims, start_idx, n_lo, bsp, bbox);
bbox[2 * cut_dim + 1] = hv;
/* Update bounding box before call to higher subset and restore after */
bbox[2 * cut_dim] = cut_val;
root->right_child = (struct Node_float *)construct_subtree_float(pa, pidx, no_dims, start_idx + n_lo, n - n_lo, bsp, bbox);
bbox[2 * cut_dim] = lv;
}
return root;
}
/************************************************
Construct a tree over data points.
Params:
pa : data points
no_dims: number of dimensions
n : number of data points
bsp : number of points per leaf
************************************************/
Tree_float* construct_tree_float(float *pa, int8_t no_dims, uint32_t n, uint32_t bsp)
{
Tree_float *tree = (Tree_float *)malloc(sizeof(Tree_float));
tree->no_dims = no_dims;
/* Initialize permutation array */
uint32_t *pidx = (uint32_t *)malloc(sizeof(uint32_t) * n);
for (uint32_t i = 0; i < n; i++)
{
pidx[i] = i;
}
float *bbox = (float *)malloc(2 * sizeof(float) * no_dims);
get_bounding_box_float(pa, pidx, no_dims, n, bbox);
tree->bbox = bbox;
/* Construct subtree on full dataset */
tree->root = (struct Node_float *)construct_subtree_float(pa, pidx, no_dims, 0, n, bsp, bbox);
tree->pidx = pidx;
return tree;
}
/************************************************
Create a tree node.
Params:
start_idx : index of first data point to use
n : number of data points
************************************************/
Node_float* create_node_float(uint32_t start_idx, uint32_t n, int is_leaf)
{
Node_float *new_node;
if (is_leaf)
{
/*
Allocate only the part of the struct that will be used in a leaf node.
This relies on the C99 specification of struct layout conservation and padding and
that dereferencing is never attempted for the node pointers in a leaf.
*/
new_node = (Node_float *)malloc(sizeof(Node_float) - 2 * sizeof(Node_float *));
}
else
{
new_node = (Node_float *)malloc(sizeof(Node_float));
}
new_node->n = n;
new_node->start_idx = start_idx;
return new_node;
}
/************************************************
Delete subtree
Params:
root : root node of subtree to delete
************************************************/
void delete_subtree_float(Node_float *root)
{
if (root->cut_dim != -1)
{
delete_subtree_float((Node_float *)root->left_child);
delete_subtree_float((Node_float *)root->right_child);
}
free(root);
}
/************************************************
Delete tree
Params:
tree : Tree struct of kd tree
************************************************/
void delete_tree_float(Tree_float *tree)
{
delete_subtree_float((Node_float *)tree->root);
free(tree->bbox);
free(tree->pidx);
free(tree);
}
/************************************************
Print
************************************************/
void print_tree_float(Node_float *root, int level)
{
for (int i = 0; i < level; i++)
{
printf(" ");
}
printf("(cut_val: %f, cut_dim: %i)\n", root->cut_val, root->cut_dim);
if (root->cut_dim != -1)
print_tree_float((Node_float *)root->left_child, level + 1);
if (root->cut_dim != -1)
print_tree_float((Node_float *)root->right_child, level + 1);
}
/************************************************
Calculate squared cartesian distance between points
Params:
point1_coord : point 1
point2_coord : point 2
************************************************/
float calc_dist_float(float *point1_coord, float *point2_coord, int8_t no_dims)
{
/* Calculate squared distance */
float dist = 0, dim_dist;
for (int8_t i = 0; i < no_dims; i++)
{
dim_dist = point2_coord[i] - point1_coord[i];
dist += dim_dist * dim_dist;
}
return dist;
}
/************************************************
Get squared distance from point to cube in specified dimension
Params:
dim : dimension
point_coord : cartesian coordinates of point
bbox : cube
************************************************/
float get_cube_offset_float(int8_t dim, float *point_coord, float *bbox)
{
float dim_coord = point_coord[dim];
if (dim_coord < bbox[2 * dim])
{
/* Left of cube in dimension */
return dim_coord - bbox[2 * dim];
}
else if (dim_coord > bbox[2 * dim + 1])
{
/* Right of cube in dimension */
return dim_coord - bbox[2 * dim + 1];
}
else
{
/* Inside cube in dimension */
return 0.;
}
}
/************************************************
Get minimum squared distance between point and cube.
Params:
point_coord : cartesian coordinates of point
no_dims : number of dimensions
bbox : cube
************************************************/
float get_min_dist_float(float *point_coord, int8_t no_dims, float *bbox)
{
float cube_offset = 0, cube_offset_dim;
for (int8_t i = 0; i < no_dims; i++)
{
cube_offset_dim = get_cube_offset_float(i, point_coord, bbox);
cube_offset += cube_offset_dim * cube_offset_dim;
}
return cube_offset;
}
/************************************************
Search a leaf node for closest point
Params:
pa : data points
pidx : permutation index of data points
no_dims : number of dimensions
start_idx : index of first data point to use
size : number of data points
point_coord : query point
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_leaf_float(float *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, float *restrict point_coord,
uint32_t k, uint32_t *restrict closest_idx, float *restrict closest_dist)
{
float cur_dist;
/* Loop through all points in leaf */
for (uint32_t i = 0; i < n; i++)
{
/* Get distance to query point */
cur_dist = calc_dist_float(&PA(start_idx + i, 0), point_coord, no_dims);
/* Update closest info if new point is closest so far*/
if (cur_dist < closest_dist[k - 1])
{
insert_point_float(closest_idx, closest_dist, pidx[start_idx + i], cur_dist, k);
}
}
}
/************************************************
Search subtree for nearest to query point
Params:
root : root node of subtree
pa : data points
pidx : permutation index of data points
no_dims : number of dimensions
point_coord : query point
min_dist : minumum distance to nearest neighbour
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_splitnode_float(Node_float *root, float *pa, uint32_t *pidx, int8_t no_dims, float *point_coord,
float min_dist, uint32_t k, float distance_upper_bound, float eps_fac, uint32_t *closest_idx, float *closest_dist)
{
int8_t dim;
float dist_left, dist_right;
float new_offset;
float box_diff;
/* Skip if distance bound exeeded */
if (min_dist > distance_upper_bound)
{
return;
}
dim = root->cut_dim;
/* Handle leaf node */
if (dim == -1)
{
search_leaf_float(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, closest_idx, closest_dist);
return;
}
/* Get distance to cutting plane */
new_offset = point_coord[dim] - root->cut_val;
if (new_offset < 0)
{
/* Left of cutting plane */
dist_left = min_dist;
if (dist_left < closest_dist[k - 1] * eps_fac)
{
/* Search left subtree if minimum distance is below limit */
search_splitnode_float((Node_float *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
/* Right of cutting plane. Update minimum distance.
See Algorithms for Fast Vector Quantization
Sunil Arya and David M. Mount. */
box_diff = root->cut_bounds_lv - point_coord[dim];
if (box_diff < 0)
{
box_diff = 0;
}
dist_right = min_dist - box_diff * box_diff + new_offset * new_offset;
if (dist_right < closest_dist[k - 1] * eps_fac)
{
/* Search right subtree if minimum distance is below limit*/
search_splitnode_float((Node_float *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
}
else
{
/* Right of cutting plane */
dist_right = min_dist;
if (dist_right < closest_dist[k - 1] * eps_fac)
{
/* Search right subtree if minimum distance is below limit*/
search_splitnode_float((Node_float *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
/* Left of cutting plane. Update minimum distance.
See Algorithms for Fast Vector Quantization
Sunil Arya and David M. Mount. */
box_diff = point_coord[dim] - root->cut_bounds_hv;
if (box_diff < 0)
{
box_diff = 0;
}
dist_left = min_dist - box_diff * box_diff + new_offset * new_offset;
if (dist_left < closest_dist[k - 1] * eps_fac)
{
/* Search left subtree if minimum distance is below limit*/
search_splitnode_float((Node_float *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
}
}
/************************************************
Search for nearest neighbour for a set of query points
Params:
tree : Tree struct of kd tree
pa : data points
pidx : permutation index of data points
point_coords : query points
num_points : number of query points
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_tree_float(Tree_float *tree, float *pa, float *point_coords,
uint32_t num_points, uint32_t k, float distance_upper_bound,
float eps, uint32_t *closest_idxs, float *closest_dists)
{
float min_dist;
float eps_fac = 1 / ((1 + eps) * (1 + eps));
int8_t no_dims = tree->no_dims;
float *bbox = tree->bbox;
uint32_t *pidx = tree->pidx;
Node_float *root = (Node_float *)tree->root;
/* Queries are OpenMP enabled */
#pragma omp parallel
{
/* The low chunk size is important to avoid L2 cache trashing
for spatial coherent query datasets
*/
#pragma omp for schedule(static, 100) nowait
for (uint32_t i = 0; i < num_points; i++)
{
for (uint32_t j = 0; j < k; j++)
{
closest_idxs[i * k + j] = UINT32_MAX;
closest_dists[i * k + j] = DBL_MAX;
}
min_dist = get_min_dist_float(point_coords + no_dims * i, no_dims, bbox);
search_splitnode_float(root, pa, pidx, no_dims, point_coords + no_dims * i, min_dist,
k, distance_upper_bound, eps_fac, &closest_idxs[i * k], &closest_dists[i * k]);
}
}
}
/************************************************
Insert point into priority queue
Params:
closest_idx : index queue
closest_dist : distance queue
pidx : permutation index of data points
cur_dist : distance to point inserted
k : number of neighbours
************************************************/
void insert_point_double(uint32_t *closest_idx, double *closest_dist, uint32_t pidx, double cur_dist, uint32_t k)
{
int i;
for (i = k - 1; i > 0; i--)
{
if (closest_dist[i - 1] > cur_dist)
{
closest_dist[i] = closest_dist[i - 1];
closest_idx[i] = closest_idx[i - 1];
}
else
{
break;
}
}
closest_idx[i] = pidx;
closest_dist[i] = cur_dist;
}
/************************************************
Get the bounding box of a set of points
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
n : number of points
bbox : bounding box (return)
************************************************/
void get_bounding_box_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, double *bbox)
{
double cur;
int8_t bbox_idx;
/* Use first data point to initialize */
for (int8_t i = 0; i < no_dims; i++)
{
bbox[2 * i] = bbox[2 * i + 1] = PA(0, i);
}
/* Update using rest of data points */
for (uint32_t i = 1; i < n; i++)
{
for (int8_t j = 0; j < no_dims; j++)
{
bbox_idx = 2 * j;
cur = PA(i, j);
if (cur < bbox[bbox_idx])
{
bbox[bbox_idx] = cur;
}
else if (cur > bbox[bbox_idx + 1])
{
bbox[bbox_idx + 1] = cur;
}
}
}
}
/************************************************
Partition a range of data points by manipulation the permutation index.
The sliding midpoint rule is used for the partitioning.
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
start_idx : index of first data point to use
n : number of data points
bbox : bounding box of data points
cut_dim : dimension used for partition (return)
cut_val : value of cutting point (return)
n_lo : number of point below cutting plane (return)
************************************************/
int partition_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, double *bbox, int8_t *cut_dim, double *cut_val, uint32_t *n_lo)
{
int8_t dim = 0;
uint32_t p, q;
double size = 0, min_val, max_val, split, side_len, cur_val;
uint32_t end_idx = start_idx + n - 1;
/* Find largest bounding box side */
for (int8_t i = 0; i < no_dims; i++)
{
side_len = bbox[2 * i + 1] - bbox[2 * i];
if (side_len > size)
{
dim = i;
size = side_len;
}
}
min_val = bbox[2 * dim];
max_val = bbox[2 * dim + 1];
/* Check for zero length or inconsistent */
if (min_val >= max_val)
return 1;
/* Use middle for splitting */
split = (min_val + max_val) / 2;
/* Partition all data points around middle */
p = start_idx;
q = end_idx;
while (p <= q)
{
if (PA(p, dim) < split)
{
p++;
}
else if (PA(q, dim) >= split)
{
/* Guard for underflow */
if (q > 0)
{
q--;
}
else
{
break;
}
}
else
{
PASWAP(p, q);
p++;
q--;
}
}
/* Check for empty splits */
if (p == start_idx)
{
/* No points less than split.
Split at lowest point instead.
Minimum 1 point will be in lower box.
*/
uint32_t j = start_idx;
split = PA(j, dim);
for (uint32_t i = start_idx + 1; i <= end_idx; i++)
{
/* Find lowest point */
cur_val = PA(i, dim);
if (cur_val < split)
{
j = i;
split = cur_val;
}
}
PASWAP(j, start_idx);
p = start_idx + 1;
}
else if (p == end_idx + 1)
{
/* No points greater than split.
Split at highest point instead.
Minimum 1 point will be in higher box.
*/
uint32_t j = end_idx;
split = PA(j, dim);
for (uint32_t i = start_idx; i < end_idx; i++)
{
/* Find highest point */
cur_val = PA(i, dim);
if (cur_val > split)
{
j = i;
split = cur_val;
}
}
PASWAP(j, end_idx);
p = end_idx;
}
/* Set return values */
*cut_dim = dim;
*cut_val = split;
*n_lo = p - start_idx;
return 0;
}
/************************************************
Construct a sub tree over a range of data points.
Params:
pa : data points
pidx : permutation index of data points
no_dims: number of dimensions
start_idx : index of first data point to use
n : number of data points
bsp : number of points per leaf
bbox : bounding box of set of data points
************************************************/
Node_double* construct_subtree_double(double *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, double *bbox)
{
/* Create new node */
int is_leaf = (n <= bsp);
Node_double *root = create_node_double(start_idx, n, is_leaf);
if (is_leaf)
{
/* Make leaf node */
root->cut_dim = -1;
}
else
{
/* Make split node */
int rval;
int8_t cut_dim;
uint32_t n_lo;
double cut_val;
/* Partition data set and set node info */
rval = partition_double(pa, pidx, no_dims, start_idx, n, bbox, &cut_dim, &cut_val, &n_lo);
if (rval == 1)
{
root->cut_dim = -1;
return root;
}
root->cut_val = cut_val;
root->cut_dim = cut_dim;
/* Recurse on both subsets */
double lv = bbox[2 * cut_dim];
double hv = bbox[2 * cut_dim + 1];
/* Set bounds for cut dimension */
root->cut_bounds_lv = lv;
root->cut_bounds_hv = hv;
/* Update bounding box before call to lower subset and restore after */
bbox[2 * cut_dim + 1] = cut_val;
root->left_child = (struct Node_double *)construct_subtree_double(pa, pidx, no_dims, start_idx, n_lo, bsp, bbox);
bbox[2 * cut_dim + 1] = hv;
/* Update bounding box before call to higher subset and restore after */
bbox[2 * cut_dim] = cut_val;
root->right_child = (struct Node_double *)construct_subtree_double(pa, pidx, no_dims, start_idx + n_lo, n - n_lo, bsp, bbox);
bbox[2 * cut_dim] = lv;
}
return root;
}
/************************************************
Construct a tree over data points.
Params:
pa : data points
no_dims: number of dimensions
n : number of data points
bsp : number of points per leaf
************************************************/
Tree_double* construct_tree_double(double *pa, int8_t no_dims, uint32_t n, uint32_t bsp)
{
Tree_double *tree = (Tree_double *)malloc(sizeof(Tree_double));
tree->no_dims = no_dims;
/* Initialize permutation array */
uint32_t *pidx = (uint32_t *)malloc(sizeof(uint32_t) * n);
for (uint32_t i = 0; i < n; i++)
{
pidx[i] = i;
}
double *bbox = (double *)malloc(2 * sizeof(double) * no_dims);
get_bounding_box_double(pa, pidx, no_dims, n, bbox);
tree->bbox = bbox;
/* Construct subtree on full dataset */
tree->root = (struct Node_double *)construct_subtree_double(pa, pidx, no_dims, 0, n, bsp, bbox);
tree->pidx = pidx;
return tree;
}
/************************************************
Create a tree node.
Params:
start_idx : index of first data point to use
n : number of data points
************************************************/
Node_double* create_node_double(uint32_t start_idx, uint32_t n, int is_leaf)
{
Node_double *new_node;
if (is_leaf)
{
/*
Allocate only the part of the struct that will be used in a leaf node.
This relies on the C99 specification of struct layout conservation and padding and
that dereferencing is never attempted for the node pointers in a leaf.
*/
new_node = (Node_double *)malloc(sizeof(Node_double) - 2 * sizeof(Node_double *));
}
else
{
new_node = (Node_double *)malloc(sizeof(Node_double));
}
new_node->n = n;
new_node->start_idx = start_idx;
return new_node;
}
/************************************************
Delete subtree
Params:
root : root node of subtree to delete
************************************************/
void delete_subtree_double(Node_double *root)
{
if (root->cut_dim != -1)
{
delete_subtree_double((Node_double *)root->left_child);
delete_subtree_double((Node_double *)root->right_child);
}
free(root);
}
/************************************************
Delete tree
Params:
tree : Tree struct of kd tree
************************************************/
void delete_tree_double(Tree_double *tree)
{
delete_subtree_double((Node_double *)tree->root);
free(tree->bbox);
free(tree->pidx);
free(tree);
}
/************************************************
Print
************************************************/
void print_tree_double(Node_double *root, int level)
{
for (int i = 0; i < level; i++)
{
printf(" ");
}
printf("(cut_val: %f, cut_dim: %i)\n", root->cut_val, root->cut_dim);
if (root->cut_dim != -1)
print_tree_double((Node_double *)root->left_child, level + 1);
if (root->cut_dim != -1)
print_tree_double((Node_double *)root->right_child, level + 1);
}
/************************************************
Calculate squared cartesian distance between points
Params:
point1_coord : point 1
point2_coord : point 2
************************************************/
double calc_dist_double(double *point1_coord, double *point2_coord, int8_t no_dims)
{
/* Calculate squared distance */
double dist = 0, dim_dist;
for (int8_t i = 0; i < no_dims; i++)
{
dim_dist = point2_coord[i] - point1_coord[i];
dist += dim_dist * dim_dist;
}
return dist;
}
/************************************************
Get squared distance from point to cube in specified dimension
Params:
dim : dimension
point_coord : cartesian coordinates of point
bbox : cube
************************************************/
double get_cube_offset_double(int8_t dim, double *point_coord, double *bbox)
{
double dim_coord = point_coord[dim];
if (dim_coord < bbox[2 * dim])
{
/* Left of cube in dimension */
return dim_coord - bbox[2 * dim];
}
else if (dim_coord > bbox[2 * dim + 1])
{
/* Right of cube in dimension */
return dim_coord - bbox[2 * dim + 1];
}
else
{
/* Inside cube in dimension */
return 0.;
}
}
/************************************************
Get minimum squared distance between point and cube.
Params:
point_coord : cartesian coordinates of point
no_dims : number of dimensions
bbox : cube
************************************************/
double get_min_dist_double(double *point_coord, int8_t no_dims, double *bbox)
{
double cube_offset = 0, cube_offset_dim;
for (int8_t i = 0; i < no_dims; i++)
{
cube_offset_dim = get_cube_offset_double(i, point_coord, bbox);
cube_offset += cube_offset_dim * cube_offset_dim;
}
return cube_offset;
}
/************************************************
Search a leaf node for closest point
Params:
pa : data points
pidx : permutation index of data points
no_dims : number of dimensions
start_idx : index of first data point to use
size : number of data points
point_coord : query point
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_leaf_double(double *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, double *restrict point_coord,
uint32_t k, uint32_t *restrict closest_idx, double *restrict closest_dist)
{
double cur_dist;
/* Loop through all points in leaf */
for (uint32_t i = 0; i < n; i++)
{
/* Get distance to query point */
cur_dist = calc_dist_double(&PA(start_idx + i, 0), point_coord, no_dims);
/* Update closest info if new point is closest so far*/
if (cur_dist < closest_dist[k - 1])
{
insert_point_double(closest_idx, closest_dist, pidx[start_idx + i], cur_dist, k);
}
}
}
/************************************************
Search subtree for nearest to query point
Params:
root : root node of subtree
pa : data points
pidx : permutation index of data points
no_dims : number of dimensions
point_coord : query point
min_dist : minumum distance to nearest neighbour
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_splitnode_double(Node_double *root, double *pa, uint32_t *pidx, int8_t no_dims, double *point_coord,
double min_dist, uint32_t k, double distance_upper_bound, double eps_fac, uint32_t *closest_idx, double *closest_dist)
{
int8_t dim;
double dist_left, dist_right;
double new_offset;
double box_diff;
/* Skip if distance bound exeeded */
if (min_dist > distance_upper_bound)
{
return;
}
dim = root->cut_dim;
/* Handle leaf node */
if (dim == -1)
{
search_leaf_double(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, closest_idx, closest_dist);
return;
}
/* Get distance to cutting plane */
new_offset = point_coord[dim] - root->cut_val;
if (new_offset < 0)
{
/* Left of cutting plane */
dist_left = min_dist;
if (dist_left < closest_dist[k - 1] * eps_fac)
{
/* Search left subtree if minimum distance is below limit */
search_splitnode_double((Node_double *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
/* Right of cutting plane. Update minimum distance.
See Algorithms for Fast Vector Quantization
Sunil Arya and David M. Mount. */
box_diff = root->cut_bounds_lv - point_coord[dim];
if (box_diff < 0)
{
box_diff = 0;
}
dist_right = min_dist - box_diff * box_diff + new_offset * new_offset;
if (dist_right < closest_dist[k - 1] * eps_fac)
{
/* Search right subtree if minimum distance is below limit*/
search_splitnode_double((Node_double *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
}
else
{
/* Right of cutting plane */
dist_right = min_dist;
if (dist_right < closest_dist[k - 1] * eps_fac)
{
/* Search right subtree if minimum distance is below limit*/
search_splitnode_double((Node_double *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
/* Left of cutting plane. Update minimum distance.
See Algorithms for Fast Vector Quantization
Sunil Arya and David M. Mount. */
box_diff = point_coord[dim] - root->cut_bounds_hv;
if (box_diff < 0)
{
box_diff = 0;
}
dist_left = min_dist - box_diff * box_diff + new_offset * new_offset;
if (dist_left < closest_dist[k - 1] * eps_fac)
{
/* Search left subtree if minimum distance is below limit*/
search_splitnode_double((Node_double *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, closest_idx, closest_dist);
}
}
}
/************************************************
Search for nearest neighbour for a set of query points
Params:
tree : Tree struct of kd tree
pa : data points
pidx : permutation index of data points
point_coords : query points
num_points : number of query points
closest_idx : index of closest data point found (return)
closest_dist : distance to closest point (return)
************************************************/
void search_tree_double(Tree_double *tree, double *pa, double *point_coords,
uint32_t num_points, uint32_t k, double distance_upper_bound,
double eps, uint32_t *closest_idxs, double *closest_dists)
{
double min_dist;
double eps_fac = 1 / ((1 + eps) * (1 + eps));
int8_t no_dims = tree->no_dims;
double *bbox = tree->bbox;
uint32_t *pidx = tree->pidx;
Node_double *root = (Node_double *)tree->root;
/* Queries are OpenMP enabled */
#pragma omp parallel
{
/* The low chunk size is important to avoid L2 cache trashing
for spatial coherent query datasets
*/
#pragma omp for schedule(static, 100) nowait
for (uint32_t i = 0; i < num_points; i++)
{
for (uint32_t j = 0; j < k; j++)
{
closest_idxs[i * k + j] = UINT32_MAX;
closest_dists[i * k + j] = DBL_MAX;
}
min_dist = get_min_dist_double(point_coords + no_dims * i, no_dims, bbox);
search_splitnode_double(root, pa, pidx, no_dims, point_coords + no_dims * i, min_dist,
k, distance_upper_bound, eps_fac, &closest_idxs[i * k], &closest_dists[i * k]);
}
}
}
pykdtree-0.2/pykdtree/kdtree.c 0000644 0007675 0007675 00001253567 12152037506 016123 0 ustar esn esn 0000000 0000000 /* Generated by Cython 0.18 on Tue May 28 10:49:15 2013 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
#ifndef Py_PYTHON_H
#error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02040000
#error Cython requires Python 2.4+.
#else
#include /* For offsetof */
#ifndef offsetof
#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
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#if !defined(WIN32) && !defined(MS_WINDOWS)
#ifndef __stdcall
#define __stdcall
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#ifndef __cdecl
#define __cdecl
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#ifndef __fastcall
#define __fastcall
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#ifndef DL_IMPORT
#define DL_IMPORT(t) t
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#ifndef DL_EXPORT
#define DL_EXPORT(t) t
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#ifndef PY_LONG_LONG
#define PY_LONG_LONG LONG_LONG
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#ifndef Py_HUGE_VAL
#define Py_HUGE_VAL HUGE_VAL
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#ifdef PYPY_VERSION
#define CYTHON_COMPILING_IN_PYPY 1
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#define CYTHON_COMPILING_IN_PYPY 0
#define CYTHON_COMPILING_IN_CPYTHON 1
#endif
#if PY_VERSION_HEX < 0x02050000
typedef int Py_ssize_t;
#define PY_SSIZE_T_MAX INT_MAX
#define PY_SSIZE_T_MIN INT_MIN
#define PY_FORMAT_SIZE_T ""
#define CYTHON_FORMAT_SSIZE_T ""
#define PyInt_FromSsize_t(z) PyInt_FromLong(z)
#define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o)
#define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
(PyErr_Format(PyExc_TypeError, \
"expected index value, got %.200s", Py_TYPE(o)->tp_name), \
(PyObject*)0))
#define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
!PyComplex_Check(o))
#define PyIndex_Check __Pyx_PyIndex_Check
#define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
#define __PYX_BUILD_PY_SSIZE_T "i"
#else
#define __PYX_BUILD_PY_SSIZE_T "n"
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#define __Pyx_PyIndex_Check PyIndex_Check
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#define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
#define Py_TYPE(ob) (((PyObject*)(ob))->ob_type)
#define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size)
#define PyVarObject_HEAD_INIT(type, size) \
PyObject_HEAD_INIT(type) size,
#define PyType_Modified(t)
typedef struct {
void *buf;
PyObject *obj;
Py_ssize_t len;
Py_ssize_t itemsize;
int readonly;
int ndim;
char *format;
Py_ssize_t *shape;
Py_ssize_t *strides;
Py_ssize_t *suboffsets;
void *internal;
} Py_buffer;
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typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
#endif
#if PY_MAJOR_VERSION < 3
#define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
#define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#else
#define __Pyx_BUILTIN_MODULE_NAME "builtins"
#define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
#endif
#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
#define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
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#define Py_TPFLAGS_CHECKTYPES 0
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#define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
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