From e25551276596085a7eb10efe06805e417c68022c Mon Sep 17 00:00:00 2001 From: Alvin Sun Date: Wed, 5 Apr 2023 21:45:51 -0700 Subject: [PATCH 1/4] generate runs --- generate.py | 8 +- setup.py | 13 - src/common.py | 18 +- src/config.py | 20 +- src/eval.py | 7 +- src/utils/binvox_rw.py | 10 +- src/utils/libkdtree/.gitignore | 1 - src/utils/libkdtree/MANIFEST.in | 2 - src/utils/libkdtree/README | 1 - src/utils/libkdtree/README.rst | 148 - src/utils/libkdtree/__init__.py | 6 - src/utils/libkdtree/pykdtree/__init__.py | 0 src/utils/libkdtree/pykdtree/_kdtree_core.c | 1417 -- .../libkdtree/pykdtree/_kdtree_core.c.mako | 734 - src/utils/libkdtree/pykdtree/kdtree.c | 11350 ---------------- src/utils/libkdtree/pykdtree/kdtree.pyx | 280 - .../libkdtree/pykdtree/render_template.py | 7 - src/utils/libkdtree/pykdtree/test_tree.py | 353 - src/utils/libkdtree/setup.cfg | 5 - 19 files changed, 33 insertions(+), 14347 deletions(-) delete mode 100644 src/utils/libkdtree/.gitignore delete mode 100644 src/utils/libkdtree/MANIFEST.in delete mode 120000 src/utils/libkdtree/README delete mode 100644 src/utils/libkdtree/README.rst delete mode 100644 src/utils/libkdtree/__init__.py delete mode 100644 src/utils/libkdtree/pykdtree/__init__.py delete mode 100644 src/utils/libkdtree/pykdtree/_kdtree_core.c delete mode 100644 src/utils/libkdtree/pykdtree/_kdtree_core.c.mako delete mode 100644 src/utils/libkdtree/pykdtree/kdtree.c delete mode 100644 src/utils/libkdtree/pykdtree/kdtree.pyx delete mode 100644 src/utils/libkdtree/pykdtree/render_template.py delete mode 100644 src/utils/libkdtree/pykdtree/test_tree.py delete mode 100644 src/utils/libkdtree/setup.cfg diff --git a/generate.py b/generate.py index 85fc59d..1c6a679 100644 --- a/generate.py +++ b/generate.py @@ -86,7 +86,7 @@ model_dict = dataset.get_model_dict(idx) except AttributeError: model_dict = {'model': str(idx), 'category': 'n/a'} - + modelname = model_dict['model'] category_id = model_dict.get('category', 'n/a') @@ -118,7 +118,7 @@ if not os.path.exists(in_dir): os.makedirs(in_dir) - + # Timing dict time_dict = { 'idx': idx, @@ -134,7 +134,7 @@ # Also copy ground truth if cfg['generation']['copy_groundtruth']: modelpath = os.path.join( - dataset.dataset_folder, category_id, modelname, + dataset.dataset_folder, category_id, modelname, cfg['data']['watertight_file']) out_file_dict['gt'] = modelpath @@ -207,7 +207,7 @@ time_df.to_pickle(out_time_file) # Create pickle files with main statistics -time_df_class = time_df.groupby(by=['class name']).mean() +time_df_class = time_df.groupby(by=['class name']).mean(numeric_only=True) time_df_class.to_pickle(out_time_file_class) # Print results diff --git a/setup.py b/setup.py index 19bb919..c661e22 100644 --- a/setup.py +++ b/setup.py @@ -12,18 +12,6 @@ numpy_include_dir = numpy.get_include() # Extensions -# pykdtree (kd tree) -pykdtree = Extension( - 'src.utils.libkdtree.pykdtree.kdtree', - sources=[ - 'src/utils/libkdtree/pykdtree/kdtree.c', - 'src/utils/libkdtree/pykdtree/_kdtree_core.c' - ], - language='c', - extra_compile_args=['-std=c99', '-O3', '-fopenmp'], - extra_link_args=['-lgomp'], - include_dirs=[numpy_include_dir] -) # mcubes (marching cubes algorithm) mcubes_module = Extension( @@ -76,7 +64,6 @@ # Gather all extension modules ext_modules = [ - pykdtree, mcubes_module, triangle_hash_module, mise_module, diff --git a/src/common.py b/src/common.py index 21f0ede..e0822ef 100755 --- a/src/common.py +++ b/src/common.py @@ -1,6 +1,6 @@ # import multiprocessing import torch -from src.utils.libkdtree import KDTree +from pykdtree.kdtree import KDTree import numpy as np import math @@ -56,7 +56,7 @@ def chamfer_distance_naive(points1, points2): Args: points1 (numpy array): first point set - points2 (numpy array): second point set + points2 (numpy array): second point set ''' assert(points1.size() == points2.size()) batch_size, T, _ = points1.size() @@ -265,7 +265,7 @@ def normalize_3d_coordinate(p, padding=0.1): p (tensor): point padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55] ''' - + p_nor = p / (1 + padding + 10e-4) # (-0.5, 0.5) p_nor = p_nor + 0.5 # range (0, 1) # f there are outliers out of the range @@ -286,7 +286,7 @@ def normalize_coord(p, vol_range, plane='xz'): p[:, 0] = (p[:, 0] - vol_range[0][0]) / (vol_range[1][0] - vol_range[0][0]) p[:, 1] = (p[:, 1] - vol_range[0][1]) / (vol_range[1][1] - vol_range[0][1]) p[:, 2] = (p[:, 2] - vol_range[0][2]) / (vol_range[1][2] - vol_range[0][2]) - + if plane == 'xz': x = p[:, [0, 2]] elif plane =='xy': @@ -294,7 +294,7 @@ def normalize_coord(p, vol_range, plane='xz'): elif plane =='yz': x = p[:, [1, 2]] else: - x = p + x = p return x def coordinate2index(x, reso, coord_type='2d'): @@ -326,7 +326,7 @@ def coord2index(p, vol_range, reso=None, plane='xz'): ''' # normalize to [0, 1] x = normalize_coord(p, vol_range, plane=plane) - + if isinstance(x, np.ndarray): x = np.floor(x * reso).astype(int) else: #* pytorch tensor @@ -338,7 +338,7 @@ def coord2index(p, vol_range, reso=None, plane='xz'): elif x.shape[1] == 3: index = x[:, 0] + reso * (x[:, 1] + reso * x[:, 2]) index[index > reso**3] = reso**3 - + return index[None] def update_reso(reso, depth): @@ -353,7 +353,7 @@ def update_reso(reso, depth): for i in range(base): if ((reso + i) / base).is_integer(): reso = reso + i - break + break return reso def decide_total_volume_range(query_vol_metric, recep_field, unit_size, unet_depth): @@ -377,7 +377,7 @@ def decide_total_volume_range(query_vol_metric, recep_field, unit_size, unet_dep # handle the case when resolution is too large if reso > 10000: reso = 1 - + return input_vol, query_vol, reso def add_key(base, new, base_name, new_name, device=None): diff --git a/src/config.py b/src/config.py index f8347a8..df335c1 100644 --- a/src/config.py +++ b/src/config.py @@ -2,6 +2,10 @@ from torchvision import transforms from src import data from src import conv_onet +try: + from yaml import CLoader as Loader +except ImportError: + from yaml import Loader method_dict = { @@ -13,13 +17,13 @@ def load_config(path, default_path=None): ''' Loads config file. - Args: + Args: path (str): path to config file default_path (bool): whether to use default path ''' # Load configuration from file itself with open(path, 'r') as f: - cfg_special = yaml.load(f) + cfg_special = yaml.load(f, Loader=Loader) # Check if we should inherit from a config inherit_from = cfg_special.get('inherit_from') @@ -30,7 +34,7 @@ def load_config(path, default_path=None): cfg = load_config(inherit_from, default_path) elif default_path is not None: with open(default_path, 'r') as f: - cfg = yaml.load(f) + cfg = yaml.load(f, Loader=Loader) else: cfg = dict() @@ -146,7 +150,7 @@ def get_dataset(mode, cfg, return_idx=False): ) else: raise ValueError('Invalid dataset "%s"' % cfg['data']['dataset']) - + return dataset @@ -184,13 +188,13 @@ def get_inputs_field(mode, cfg): data.SubsamplePointcloud(cfg['data']['pointcloud_n']), data.PointcloudNoise(cfg['data']['pointcloud_noise']) ]) - + inputs_field = data.PatchPointCloudField( - cfg['data']['pointcloud_file'], + cfg['data']['pointcloud_file'], transform, multi_files= cfg['data']['multi_files'], ) - + elif input_type == 'voxels': inputs_field = data.VoxelsField( cfg['data']['voxels_file'] @@ -200,4 +204,4 @@ def get_inputs_field(mode, cfg): else: raise ValueError( 'Invalid input type (%s)' % input_type) - return inputs_field \ No newline at end of file + return inputs_field diff --git a/src/eval.py b/src/eval.py index 1f540da..aa1d0a2 100644 --- a/src/eval.py +++ b/src/eval.py @@ -1,8 +1,7 @@ import logging import numpy as np import trimesh -# from scipy.spatial import cKDTree -from src.utils.libkdtree import KDTree +from pykdtree.kdtree import KDTree from src.utils.libmesh import check_mesh_contains from src.common import compute_iou @@ -59,14 +58,14 @@ def eval_mesh(self, mesh, pointcloud_tgt, normals_tgt, x_max, x_min = x_max + eps, x_min - eps y_max, y_min = y_max + eps, y_min - eps z_max, z_min = z_max + eps, z_min - eps - + mask_x = (pointcloud[:, 0] <= x_max) & (pointcloud[:, 0] >= x_min) mask_y = (pointcloud[:, 1] >= y_min) # floor mask_z = (pointcloud[:, 2] <= z_max) & (pointcloud[:, 2] >= z_min) mask = mask_x & mask_y & mask_z pointcloud_new = pointcloud[mask] - # Subsample + # Subsample idx_new = np.random.randint(pointcloud_new.shape[0], size=self.n_points) pointcloud = pointcloud_new[idx_new] idx = idx[mask][idx_new] diff --git a/src/utils/binvox_rw.py b/src/utils/binvox_rw.py index c9c11d6..b2a6327 100644 --- a/src/utils/binvox_rw.py +++ b/src/utils/binvox_rw.py @@ -142,7 +142,7 @@ def read_as_3d_array(fp, fix_coords=True): # j -> y # k -> z values, counts = raw_data[::2], raw_data[1::2] - data = np.repeat(values, counts).astype(np.bool) + data = np.repeat(values, counts).astype(bool) data = data.reshape(dims) if fix_coords: # xzy to xyz TODO the right thing @@ -177,7 +177,7 @@ def read_as_coord_array(fp, fix_coords=True): end_indices = np.cumsum(counts) indices = np.concatenate(([0], end_indices[:-1])).astype(end_indices.dtype) - values = values.astype(np.bool) + values = values.astype(bool) indices = indices[values] end_indices = end_indices[values] @@ -203,7 +203,7 @@ def read_as_coord_array(fp, fix_coords=True): #return Voxels(data, dims, translate, scale, axis_order) return Voxels(np.ascontiguousarray(data), dims, translate, scale, axis_order) -def dense_to_sparse(voxel_data, dtype=np.int): +def dense_to_sparse(voxel_data, dtype=int): """ From dense representation to sparse (coordinate) representation. No coordinate reordering. """ @@ -211,14 +211,14 @@ def dense_to_sparse(voxel_data, dtype=np.int): raise ValueError('voxel_data is wrong shape; should be 3D array.') return np.asarray(np.nonzero(voxel_data), dtype) -def sparse_to_dense(voxel_data, dims, dtype=np.bool): +def sparse_to_dense(voxel_data, dims, dtype=bool): if voxel_data.ndim!=2 or voxel_data.shape[0]!=3: raise ValueError('voxel_data is wrong shape; should be 3xN array.') if np.isscalar(dims): dims = [dims]*3 dims = np.atleast_2d(dims).T # truncate to integers - xyz = voxel_data.astype(np.int) + xyz = voxel_data.astype(int) # discard voxels that fall outside dims valid_ix = ~np.any((xyz < 0) | (xyz >= dims), 0) xyz = xyz[:,valid_ix] diff --git a/src/utils/libkdtree/.gitignore b/src/utils/libkdtree/.gitignore deleted file mode 100644 index 378eac2..0000000 --- a/src/utils/libkdtree/.gitignore +++ /dev/null @@ -1 +0,0 @@ -build diff --git a/src/utils/libkdtree/MANIFEST.in b/src/utils/libkdtree/MANIFEST.in deleted file mode 100644 index 0ff2a61..0000000 --- a/src/utils/libkdtree/MANIFEST.in +++ /dev/null @@ -1,2 +0,0 @@ -exclude pykdtree/render_template.py -include LICENSE.txt diff --git a/src/utils/libkdtree/README b/src/utils/libkdtree/README deleted file mode 120000 index 92cacd2..0000000 --- a/src/utils/libkdtree/README +++ /dev/null @@ -1 +0,0 @@ -README.rst \ No newline at end of file diff --git a/src/utils/libkdtree/README.rst b/src/utils/libkdtree/README.rst deleted file mode 100644 index cb7001e..0000000 --- a/src/utils/libkdtree/README.rst +++ /dev/null @@ -1,148 +0,0 @@ -.. image:: https://travis-ci.org/storpipfugl/pykdtree.svg?branch=master - :target: https://travis-ci.org/storpipfugl/pykdtree -.. image:: https://ci.appveyor.com/api/projects/status/ubo92368ktt2d25g/branch/master - :target: https://ci.appveyor.com/project/storpipfugl/pykdtree - -======== -pykdtree -======== - -Objective ---------- -pykdtree is a kd-tree implementation for fast nearest neighbour search in Python. -The aim is to be the fastest implementation around for common use cases (low dimensions and low number of neighbours) for both tree construction and queries. - -The implementation is based on scipy.spatial.cKDTree and libANN by combining the best features from both and focus on implementation efficiency. - -The interface is similar to that of scipy.spatial.cKDTree except only Euclidean distance measure is supported. - -Queries are optionally multithreaded using OpenMP. - -Installation ------------- -Default build of pykdtree with OpenMP enabled queries using libgomp - -.. code-block:: bash - - $ cd - $ python setup.py install - -If it fails with undefined compiler flags or you want to use another OpenMP implementation please modify setup.py at the indicated point to match your system. - -Building without OpenMP support is controlled by the USE_OMP environment variable - -.. code-block:: bash - - $ cd - $ export USE_OMP=0 - $ python setup.py install - -Note evironment variables are by default not exported when using sudo so in this case do - -.. code-block:: bash - - $ USE_OMP=0 sudo -E python setup.py install - -Usage ------ -The usage of pykdtree is similar to scipy.spatial.cKDTree so for now refer to its documentation - - >>> from pykdtree.kdtree import KDTree - >>> kd_tree = KDTree(data_pts) - >>> dist, idx = kd_tree.query(query_pts, k=8) - -The number of threads to be used in OpenMP enabled queries can be controlled with the standard OpenMP environment variable OMP_NUM_THREADS. - -The **leafsize** argument (number of data points per leaf) for the tree creation can be used to control the memory overhead of the kd-tree. pykdtree uses a default **leafsize=16**. -Increasing **leafsize** will reduce the memory overhead and construction time but increase query time. - -pykdtree accepts data in double precision (numpy.float64) or single precision (numpy.float32) floating point. If data of another type is used an internal copy in double precision is made resulting in a memory overhead. If the kd-tree is constructed on single precision data the query points must be single precision as well. - -Benchmarks ----------- -Comparison with scipy.spatial.cKDTree and libANN. This benchmark is on geospatial 3D data with 10053632 data points and 4276224 query points. The results are indexed relative to the construction time of scipy.spatial.cKDTree. A leafsize of 10 (scipy.spatial.cKDTree default) is used. - -Note: libANN is *not* thread safe. In this benchmark libANN is compiled with "-O3 -funroll-loops -ffast-math -fprefetch-loop-arrays" in order to achieve optimum performance. - -================== ===================== ====== ======== ================== -Operation scipy.spatial.cKDTree libANN pykdtree pykdtree 4 threads ------------------- --------------------- ------ -------- ------------------ - -Construction 100 304 96 96 - -query 1 neighbour 1267 294 223 70 - -Total 1 neighbour 1367 598 319 166 - -query 8 neighbours 2193 625 449 143 - -Total 8 neighbours 2293 929 545 293 -================== ===================== ====== ======== ================== - -Looking at the combined construction and query this gives the following performance improvement relative to scipy.spatial.cKDTree - -========== ====== ======== ================== -Neighbours libANN pykdtree pykdtree 4 threads ----------- ------ -------- ------------------ -1 129% 329% 723% - -8 147% 320% 682% -========== ====== ======== ================== - -Note: mileage will vary with the dataset at hand and computer architecture. - -Test ----- -Run the unit tests using nosetest - -.. code-block:: bash - - $ cd - $ python setup.py nosetests - -Installing on AppVeyor ----------------------- - -Pykdtree requires the "stdint.h" header file which is not available on certain -versions of Windows or certain Windows compilers including those on the -continuous integration platform AppVeyor. To get around this the header file(s) -can be downloaded and placed in the correct "include" directory. This can -be done by adding the `anaconda/missing-headers.ps1` script to your repository -and running it the install step of `appveyor.yml`: - - # install missing headers that aren't included with MSVC 2008 - # https://github.com/omnia-md/conda-recipes/pull/524 - - "powershell ./appveyor/missing-headers.ps1" - -In addition to this, AppVeyor does not support OpenMP so this feature must be -turned off by adding the following to `appveyor.yml` in the -`environment` section: - - environment: - global: - # Don't build with openmp because it isn't supported in appveyor's compilers - USE_OMP: "0" - -Changelog ---------- -v1.3.1 : Fix masking in the "query" method introduced in 1.3.0 - -v1.3.0 : Keyword argument "mask" added to "query" method. OpenMP compilation now works for MS Visual Studio compiler - -v1.2.2 : Build process fixes - -v1.2.1 : Fixed OpenMP thread safety issue introduced in v1.2.0 - -v1.2.0 : 64 and 32 bit MSVC Windows support added - -v1.1.1 : Same as v1.1 release due to incorrect pypi release - -v1.1 : Build process improvements. Add data attribute to kdtree class for scipy interface compatibility - -v1.0 : Switched license from GPLv3 to LGPLv3 - -v0.3 : Avoid zipping of installed egg - -v0.2 : Reduced memory footprint. Can now handle single precision data internally avoiding copy conversion to double precision. Default leafsize changed from 10 to 16 as this reduces the memory footprint and makes it a cache line multiplum (negligible if any query performance observed in benchmarks). Reduced memory allocation for leaf nodes. Applied patch for building on OS X. - -v0.1 : Initial version. diff --git a/src/utils/libkdtree/__init__.py b/src/utils/libkdtree/__init__.py deleted file mode 100644 index cbd34df..0000000 --- a/src/utils/libkdtree/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .pykdtree.kdtree import KDTree - - -__all__ = [ - KDTree -] diff --git a/src/utils/libkdtree/pykdtree/__init__.py b/src/utils/libkdtree/pykdtree/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/src/utils/libkdtree/pykdtree/_kdtree_core.c b/src/utils/libkdtree/pykdtree/_kdtree_core.c deleted file mode 100644 index aebb816..0000000 --- a/src/utils/libkdtree/pykdtree/_kdtree_core.c +++ /dev/null @@ -1,1417 +0,0 @@ -/* -pykdtree, Fast kd-tree implementation with OpenMP-enabled queries - -Copyright (C) 2013 - present Esben S. Nielsen - -This program is free software: you can redistribute it and/or modify it under -the terms of the GNU Lesser 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 Lesser General Public License for more -details. - -You should have received a copy of the GNU Lesser 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; } - -#ifdef _MSC_VER -#define restrict __restrict -#endif - - -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_leaf_float_mask(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, uint8_t *restrict mask, 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, uint8_t *mask, 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, uint8_t *mask, 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_leaf_double_mask(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, uint8_t *restrict mask, 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, uint8_t *mask, 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, uint8_t *mask, 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, i, j; - uint32_t i2; - - /* Use first data point to initialize */ - for (i = 0; i < no_dims; i++) - { - bbox[2 * i] = bbox[2 * i + 1] = PA(0, i); - } - - /* Update using rest of data points */ - for (i2 = 1; i2 < n; i2++) - { - for (j = 0; j < no_dims; j++) - { - bbox_idx = 2 * j; - cur = PA(i2, 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, i; - uint32_t p, q, i2; - 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 (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 (i2 = start_idx + 1; i2 <= end_idx; i2++) - { - /* Find lowest point */ - cur_val = PA(i2, dim); - if (cur_val < split) - { - j = i2; - 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 (i2 = start_idx; i2 < end_idx; i2++) - { - /* Find highest point */ - cur_val = PA(i2, dim); - if (cur_val > split) - { - j = i2; - 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); - int rval; - int8_t cut_dim; - uint32_t n_lo; - float cut_val, lv, hv; - if (is_leaf) - { - /* Make leaf node */ - root->cut_dim = -1; - } - else - { - /* Make split node */ - /* 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 */ - lv = bbox[2 * cut_dim]; - 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)); - uint32_t i; - uint32_t *pidx; - float *bbox; - - tree->no_dims = no_dims; - - /* Initialize permutation array */ - pidx = (uint32_t *)malloc(sizeof(uint32_t) * n); - for (i = 0; i < n; i++) - { - pidx[i] = i; - } - - 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) -{ - int i; - for (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; - int8_t i; - for (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; - int8_t i; - - for (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; - uint32_t i; - /* Loop through all points in leaf */ - for (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 a leaf node for closest point with data point mask -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 - mask : boolean array of invalid (True) and valid (False) data points - closest_idx : index of closest data point found (return) - closest_dist : distance to closest point (return) -************************************************/ -void search_leaf_float_mask(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, uint8_t *mask, uint32_t *restrict closest_idx, float *restrict closest_dist) -{ - float cur_dist; - uint32_t i; - /* Loop through all points in leaf */ - for (i = 0; i < n; i++) - { - /* Is this point masked out? */ - if (mask[pidx[start_idx + i]]) - { - continue; - } - /* 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 - mask : boolean array of invalid (True) and valid (False) data points - 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, uint8_t *mask, - 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) - { - if (mask) - { - search_leaf_float_mask(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, mask, closest_idx, closest_dist); - } - else - { - 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, mask, 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, mask, 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, mask, 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, mask, 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 - mask : boolean array of invalid (True) and valid (False) data 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, uint8_t *mask, 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; - uint32_t j = 0; -#if defined(_MSC_VER) && defined(_OPENMP) - int32_t i = 0; - int32_t local_num_points = (int32_t) num_points; -#else - uint32_t i; - uint32_t local_num_points = num_points; -#endif - 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 private(i, j) schedule(static, 100) nowait - for (i = 0; i < local_num_points; i++) - { - for (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, mask, &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, i, j; - uint32_t i2; - - /* Use first data point to initialize */ - for (i = 0; i < no_dims; i++) - { - bbox[2 * i] = bbox[2 * i + 1] = PA(0, i); - } - - /* Update using rest of data points */ - for (i2 = 1; i2 < n; i2++) - { - for (j = 0; j < no_dims; j++) - { - bbox_idx = 2 * j; - cur = PA(i2, 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, i; - uint32_t p, q, i2; - 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 (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 (i2 = start_idx + 1; i2 <= end_idx; i2++) - { - /* Find lowest point */ - cur_val = PA(i2, dim); - if (cur_val < split) - { - j = i2; - 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 (i2 = start_idx; i2 < end_idx; i2++) - { - /* Find highest point */ - cur_val = PA(i2, dim); - if (cur_val > split) - { - j = i2; - 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); - int rval; - int8_t cut_dim; - uint32_t n_lo; - double cut_val, lv, hv; - if (is_leaf) - { - /* Make leaf node */ - root->cut_dim = -1; - } - else - { - /* Make split node */ - /* 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 */ - lv = bbox[2 * cut_dim]; - 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)); - uint32_t i; - uint32_t *pidx; - double *bbox; - - tree->no_dims = no_dims; - - /* Initialize permutation array */ - pidx = (uint32_t *)malloc(sizeof(uint32_t) * n); - for (i = 0; i < n; i++) - { - pidx[i] = i; - } - - 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) -{ - int i; - for (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; - int8_t i; - for (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; - int8_t i; - - for (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; - uint32_t i; - /* Loop through all points in leaf */ - for (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 a leaf node for closest point with data point mask -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 - mask : boolean array of invalid (True) and valid (False) data points - closest_idx : index of closest data point found (return) - closest_dist : distance to closest point (return) -************************************************/ -void search_leaf_double_mask(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, uint8_t *mask, uint32_t *restrict closest_idx, double *restrict closest_dist) -{ - double cur_dist; - uint32_t i; - /* Loop through all points in leaf */ - for (i = 0; i < n; i++) - { - /* Is this point masked out? */ - if (mask[pidx[start_idx + i]]) - { - continue; - } - /* 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 - mask : boolean array of invalid (True) and valid (False) data points - 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, uint8_t *mask, - 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) - { - if (mask) - { - search_leaf_double_mask(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, mask, closest_idx, closest_dist); - } - else - { - 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, mask, 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, mask, 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, mask, 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, mask, 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 - mask : boolean array of invalid (True) and valid (False) data 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, uint8_t *mask, 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; - uint32_t j = 0; -#if defined(_MSC_VER) && defined(_OPENMP) - int32_t i = 0; - int32_t local_num_points = (int32_t) num_points; -#else - uint32_t i; - uint32_t local_num_points = num_points; -#endif - 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 private(i, j) schedule(static, 100) nowait - for (i = 0; i < local_num_points; i++) - { - for (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, mask, &closest_idxs[i * k], &closest_dists[i * k]); - } - } -} diff --git a/src/utils/libkdtree/pykdtree/_kdtree_core.c.mako b/src/utils/libkdtree/pykdtree/_kdtree_core.c.mako deleted file mode 100644 index a8270f5..0000000 --- a/src/utils/libkdtree/pykdtree/_kdtree_core.c.mako +++ /dev/null @@ -1,734 +0,0 @@ -/* -pykdtree, Fast kd-tree implementation with OpenMP-enabled queries - -Copyright (C) 2013 - present Esben S. Nielsen - -This program is free software: you can redistribute it and/or modify it under -the terms of the GNU Lesser 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 Lesser General Public License for more -details. - -You should have received a copy of the GNU Lesser 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; } - -#ifdef _MSC_VER -#define restrict __restrict -#endif - -% for DTYPE in ['float', 'double']: - -typedef struct -{ - ${DTYPE} cut_val; - int8_t cut_dim; - uint32_t start_idx; - uint32_t n; - ${DTYPE} cut_bounds_lv; - ${DTYPE} cut_bounds_hv; - struct Node_${DTYPE} *left_child; - struct Node_${DTYPE} *right_child; -} Node_${DTYPE}; - -typedef struct -{ - ${DTYPE} *bbox; - int8_t no_dims; - uint32_t *pidx; - struct Node_${DTYPE} *root; -} Tree_${DTYPE}; - -% endfor - -% for DTYPE in ['float', 'double']: - -void insert_point_${DTYPE}(uint32_t *closest_idx, ${DTYPE} *closest_dist, uint32_t pidx, ${DTYPE} cur_dist, uint32_t k); -void get_bounding_box_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, ${DTYPE} *bbox); -int partition_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *bbox, int8_t *cut_dim, - ${DTYPE} *cut_val, uint32_t *n_lo); -Tree_${DTYPE}* construct_tree_${DTYPE}(${DTYPE} *pa, int8_t no_dims, uint32_t n, uint32_t bsp); -Node_${DTYPE}* construct_subtree_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, ${DTYPE} *bbox); -Node_${DTYPE} * create_node_${DTYPE}(uint32_t start_idx, uint32_t n, int is_leaf); -void delete_subtree_${DTYPE}(Node_${DTYPE} *root); -void delete_tree_${DTYPE}(Tree_${DTYPE} *tree); -void print_tree_${DTYPE}(Node_${DTYPE} *root, int level); -${DTYPE} calc_dist_${DTYPE}(${DTYPE} *point1_coord, ${DTYPE} *point2_coord, int8_t no_dims); -${DTYPE} get_cube_offset_${DTYPE}(int8_t dim, ${DTYPE} *point_coord, ${DTYPE} *bbox); -${DTYPE} get_min_dist_${DTYPE}(${DTYPE} *point_coord, int8_t no_dims, ${DTYPE} *bbox); -void search_leaf_${DTYPE}(${DTYPE} *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *restrict point_coord, - uint32_t k, uint32_t *restrict closest_idx, ${DTYPE} *restrict closest_dist); -void search_leaf_${DTYPE}_mask(${DTYPE} *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *restrict point_coord, - uint32_t k, uint8_t *restrict mask, uint32_t *restrict closest_idx, ${DTYPE} *restrict closest_dist); -void search_splitnode_${DTYPE}(Node_${DTYPE} *root, ${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, ${DTYPE} *point_coord, - ${DTYPE} min_dist, uint32_t k, ${DTYPE} distance_upper_bound, ${DTYPE} eps_fac, uint8_t *mask, uint32_t * closest_idx, ${DTYPE} *closest_dist); -void search_tree_${DTYPE}(Tree_${DTYPE} *tree, ${DTYPE} *pa, ${DTYPE} *point_coords, - uint32_t num_points, uint32_t k, ${DTYPE} distance_upper_bound, - ${DTYPE} eps, uint8_t *mask, uint32_t *closest_idxs, ${DTYPE} *closest_dists); - -% endfor - -% for DTYPE in ['float', 'double']: - -/************************************************ -Insert point into priority queue -Params: - closest_idx : index queue - closest_dist : distance queue - pidx : permutation index of data points - cur_dist : distance to point inserted - k : number of neighbours -************************************************/ -void insert_point_${DTYPE}(uint32_t *closest_idx, ${DTYPE} *closest_dist, uint32_t pidx, ${DTYPE} cur_dist, uint32_t k) -{ - int i; - for (i = k - 1; i > 0; i--) - { - if (closest_dist[i - 1] > cur_dist) - { - closest_dist[i] = closest_dist[i - 1]; - closest_idx[i] = closest_idx[i - 1]; - } - else - { - break; - } - } - closest_idx[i] = pidx; - closest_dist[i] = cur_dist; -} - -/************************************************ -Get the bounding box of a set of points -Params: - pa : data points - pidx : permutation index of data points - no_dims: number of dimensions - n : number of points - bbox : bounding box (return) -************************************************/ -void get_bounding_box_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t n, ${DTYPE} *bbox) -{ - ${DTYPE} cur; - int8_t bbox_idx, i, j; - uint32_t i2; - - /* Use first data point to initialize */ - for (i = 0; i < no_dims; i++) - { - bbox[2 * i] = bbox[2 * i + 1] = PA(0, i); - } - - /* Update using rest of data points */ - for (i2 = 1; i2 < n; i2++) - { - for (j = 0; j < no_dims; j++) - { - bbox_idx = 2 * j; - cur = PA(i2, j); - if (cur < bbox[bbox_idx]) - { - bbox[bbox_idx] = cur; - } - else if (cur > bbox[bbox_idx + 1]) - { - bbox[bbox_idx + 1] = cur; - } - } - } -} - -/************************************************ -Partition a range of data points by manipulation the permutation index. -The sliding midpoint rule is used for the partitioning. -Params: - pa : data points - pidx : permutation index of data points - no_dims: number of dimensions - start_idx : index of first data point to use - n : number of data points - bbox : bounding box of data points - cut_dim : dimension used for partition (return) - cut_val : value of cutting point (return) - n_lo : number of point below cutting plane (return) -************************************************/ -int partition_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *bbox, int8_t *cut_dim, ${DTYPE} *cut_val, uint32_t *n_lo) -{ - int8_t dim = 0, i; - uint32_t p, q, i2; - ${DTYPE} 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 (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 (i2 = start_idx + 1; i2 <= end_idx; i2++) - { - /* Find lowest point */ - cur_val = PA(i2, dim); - if (cur_val < split) - { - j = i2; - 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 (i2 = start_idx; i2 < end_idx; i2++) - { - /* Find highest point */ - cur_val = PA(i2, dim); - if (cur_val > split) - { - j = i2; - 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_${DTYPE}* construct_subtree_${DTYPE}(${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, uint32_t bsp, ${DTYPE} *bbox) -{ - /* Create new node */ - int is_leaf = (n <= bsp); - Node_${DTYPE} *root = create_node_${DTYPE}(start_idx, n, is_leaf); - int rval; - int8_t cut_dim; - uint32_t n_lo; - ${DTYPE} cut_val, lv, hv; - if (is_leaf) - { - /* Make leaf node */ - root->cut_dim = -1; - } - else - { - /* Make split node */ - /* Partition data set and set node info */ - rval = partition_${DTYPE}(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 */ - lv = bbox[2 * cut_dim]; - 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_${DTYPE} *)construct_subtree_${DTYPE}(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_${DTYPE} *)construct_subtree_${DTYPE}(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_${DTYPE}* construct_tree_${DTYPE}(${DTYPE} *pa, int8_t no_dims, uint32_t n, uint32_t bsp) -{ - Tree_${DTYPE} *tree = (Tree_${DTYPE} *)malloc(sizeof(Tree_${DTYPE})); - uint32_t i; - uint32_t *pidx; - ${DTYPE} *bbox; - - tree->no_dims = no_dims; - - /* Initialize permutation array */ - pidx = (uint32_t *)malloc(sizeof(uint32_t) * n); - for (i = 0; i < n; i++) - { - pidx[i] = i; - } - - bbox = (${DTYPE} *)malloc(2 * sizeof(${DTYPE}) * no_dims); - get_bounding_box_${DTYPE}(pa, pidx, no_dims, n, bbox); - tree->bbox = bbox; - - /* Construct subtree on full dataset */ - tree->root = (struct Node_${DTYPE} *)construct_subtree_${DTYPE}(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_${DTYPE}* create_node_${DTYPE}(uint32_t start_idx, uint32_t n, int is_leaf) -{ - Node_${DTYPE} *new_node; - if (is_leaf) - { - /* - Allocate only the part of the struct that will be used in a leaf node. - This relies on the C99 specification of struct layout conservation and padding and - that dereferencing is never attempted for the node pointers in a leaf. - */ - new_node = (Node_${DTYPE} *)malloc(sizeof(Node_${DTYPE}) - 2 * sizeof(Node_${DTYPE} *)); - } - else - { - new_node = (Node_${DTYPE} *)malloc(sizeof(Node_${DTYPE})); - } - 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_${DTYPE}(Node_${DTYPE} *root) -{ - if (root->cut_dim != -1) - { - delete_subtree_${DTYPE}((Node_${DTYPE} *)root->left_child); - delete_subtree_${DTYPE}((Node_${DTYPE} *)root->right_child); - } - free(root); -} - -/************************************************ -Delete tree -Params: - tree : Tree struct of kd tree -************************************************/ -void delete_tree_${DTYPE}(Tree_${DTYPE} *tree) -{ - delete_subtree_${DTYPE}((Node_${DTYPE} *)tree->root); - free(tree->bbox); - free(tree->pidx); - free(tree); -} - -/************************************************ -Print -************************************************/ -void print_tree_${DTYPE}(Node_${DTYPE} *root, int level) -{ - int i; - for (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_${DTYPE}((Node_${DTYPE} *)root->left_child, level + 1); - if (root->cut_dim != -1) - print_tree_${DTYPE}((Node_${DTYPE} *)root->right_child, level + 1); -} - -/************************************************ -Calculate squared cartesian distance between points -Params: - point1_coord : point 1 - point2_coord : point 2 -************************************************/ -${DTYPE} calc_dist_${DTYPE}(${DTYPE} *point1_coord, ${DTYPE} *point2_coord, int8_t no_dims) -{ - /* Calculate squared distance */ - ${DTYPE} dist = 0, dim_dist; - int8_t i; - for (i = 0; i < no_dims; i++) - { - dim_dist = point2_coord[i] - point1_coord[i]; - dist += dim_dist * dim_dist; - } - return dist; -} - -/************************************************ -Get squared distance from point to cube in specified dimension -Params: - dim : dimension - point_coord : cartesian coordinates of point - bbox : cube -************************************************/ -${DTYPE} get_cube_offset_${DTYPE}(int8_t dim, ${DTYPE} *point_coord, ${DTYPE} *bbox) -{ - ${DTYPE} dim_coord = point_coord[dim]; - - if (dim_coord < bbox[2 * dim]) - { - /* Left of cube in dimension */ - return dim_coord - bbox[2 * dim]; - } - else if (dim_coord > bbox[2 * dim + 1]) - { - /* Right of cube in dimension */ - return dim_coord - bbox[2 * dim + 1]; - } - else - { - /* Inside cube in dimension */ - return 0.; - } -} - -/************************************************ -Get minimum squared distance between point and cube. -Params: - point_coord : cartesian coordinates of point - no_dims : number of dimensions - bbox : cube -************************************************/ -${DTYPE} get_min_dist_${DTYPE}(${DTYPE} *point_coord, int8_t no_dims, ${DTYPE} *bbox) -{ - ${DTYPE} cube_offset = 0, cube_offset_dim; - int8_t i; - - for (i = 0; i < no_dims; i++) - { - cube_offset_dim = get_cube_offset_${DTYPE}(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_${DTYPE}(${DTYPE} *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *restrict point_coord, - uint32_t k, uint32_t *restrict closest_idx, ${DTYPE} *restrict closest_dist) -{ - ${DTYPE} cur_dist; - uint32_t i; - /* Loop through all points in leaf */ - for (i = 0; i < n; i++) - { - /* Get distance to query point */ - cur_dist = calc_dist_${DTYPE}(&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_${DTYPE}(closest_idx, closest_dist, pidx[start_idx + i], cur_dist, k); - } - } -} - - -/************************************************ -Search a leaf node for closest point with data point mask -Params: - pa : data points - pidx : permutation index of data points - no_dims : number of dimensions - start_idx : index of first data point to use - size : number of data points - point_coord : query point - mask : boolean array of invalid (True) and valid (False) data points - closest_idx : index of closest data point found (return) - closest_dist : distance to closest point (return) -************************************************/ -void search_leaf_${DTYPE}_mask(${DTYPE} *restrict pa, uint32_t *restrict pidx, int8_t no_dims, uint32_t start_idx, uint32_t n, ${DTYPE} *restrict point_coord, - uint32_t k, uint8_t *mask, uint32_t *restrict closest_idx, ${DTYPE} *restrict closest_dist) -{ - ${DTYPE} cur_dist; - uint32_t i; - /* Loop through all points in leaf */ - for (i = 0; i < n; i++) - { - /* Is this point masked out? */ - if (mask[pidx[start_idx + i]]) - { - continue; - } - /* Get distance to query point */ - cur_dist = calc_dist_${DTYPE}(&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_${DTYPE}(closest_idx, closest_dist, pidx[start_idx + i], cur_dist, k); - } - } -} - -/************************************************ -Search subtree for nearest to query point -Params: - root : root node of subtree - pa : data points - pidx : permutation index of data points - no_dims : number of dimensions - point_coord : query point - min_dist : minumum distance to nearest neighbour - mask : boolean array of invalid (True) and valid (False) data points - closest_idx : index of closest data point found (return) - closest_dist : distance to closest point (return) -************************************************/ -void search_splitnode_${DTYPE}(Node_${DTYPE} *root, ${DTYPE} *pa, uint32_t *pidx, int8_t no_dims, ${DTYPE} *point_coord, - ${DTYPE} min_dist, uint32_t k, ${DTYPE} distance_upper_bound, ${DTYPE} eps_fac, uint8_t *mask, - uint32_t *closest_idx, ${DTYPE} *closest_dist) -{ - int8_t dim; - ${DTYPE} dist_left, dist_right; - ${DTYPE} new_offset; - ${DTYPE} box_diff; - - /* Skip if distance bound exeeded */ - if (min_dist > distance_upper_bound) - { - return; - } - - dim = root->cut_dim; - - /* Handle leaf node */ - if (dim == -1) - { - if (mask) - { - search_leaf_${DTYPE}_mask(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, mask, closest_idx, closest_dist); - } - else - { - search_leaf_${DTYPE}(pa, pidx, no_dims, root->start_idx, root->n, point_coord, k, closest_idx, closest_dist); - } - return; - } - - /* Get distance to cutting plane */ - new_offset = point_coord[dim] - root->cut_val; - - if (new_offset < 0) - { - /* Left of cutting plane */ - dist_left = min_dist; - if (dist_left < closest_dist[k - 1] * eps_fac) - { - /* Search left subtree if minimum distance is below limit */ - search_splitnode_${DTYPE}((Node_${DTYPE} *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, mask, closest_idx, closest_dist); - } - - /* Right of cutting plane. Update minimum distance. - See Algorithms for Fast Vector Quantization - Sunil Arya and David M. Mount. */ - box_diff = root->cut_bounds_lv - point_coord[dim]; - if (box_diff < 0) - { - box_diff = 0; - } - dist_right = min_dist - box_diff * box_diff + new_offset * new_offset; - if (dist_right < closest_dist[k - 1] * eps_fac) - { - /* Search right subtree if minimum distance is below limit*/ - search_splitnode_${DTYPE}((Node_${DTYPE} *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, mask, closest_idx, closest_dist); - } - } - else - { - /* Right of cutting plane */ - dist_right = min_dist; - if (dist_right < closest_dist[k - 1] * eps_fac) - { - /* Search right subtree if minimum distance is below limit*/ - search_splitnode_${DTYPE}((Node_${DTYPE} *)root->right_child, pa, pidx, no_dims, point_coord, dist_right, k, distance_upper_bound, eps_fac, mask, closest_idx, closest_dist); - } - - /* Left of cutting plane. Update minimum distance. - See Algorithms for Fast Vector Quantization - Sunil Arya and David M. Mount. */ - box_diff = point_coord[dim] - root->cut_bounds_hv; - if (box_diff < 0) - { - box_diff = 0; - } - dist_left = min_dist - box_diff * box_diff + new_offset * new_offset; - if (dist_left < closest_dist[k - 1] * eps_fac) - { - /* Search left subtree if minimum distance is below limit*/ - search_splitnode_${DTYPE}((Node_${DTYPE} *)root->left_child, pa, pidx, no_dims, point_coord, dist_left, k, distance_upper_bound, eps_fac, mask, closest_idx, closest_dist); - } - } -} - -/************************************************ -Search for nearest neighbour for a set of query points -Params: - tree : Tree struct of kd tree - pa : data points - pidx : permutation index of data points - point_coords : query points - num_points : number of query points - mask : boolean array of invalid (True) and valid (False) data points - closest_idx : index of closest data point found (return) - closest_dist : distance to closest point (return) -************************************************/ -void search_tree_${DTYPE}(Tree_${DTYPE} *tree, ${DTYPE} *pa, ${DTYPE} *point_coords, - uint32_t num_points, uint32_t k, ${DTYPE} distance_upper_bound, - ${DTYPE} eps, uint8_t *mask, uint32_t *closest_idxs, ${DTYPE} *closest_dists) -{ - ${DTYPE} min_dist; - ${DTYPE} eps_fac = 1 / ((1 + eps) * (1 + eps)); - int8_t no_dims = tree->no_dims; - ${DTYPE} *bbox = tree->bbox; - uint32_t *pidx = tree->pidx; - uint32_t j = 0; -#if defined(_MSC_VER) && defined(_OPENMP) - int32_t i = 0; - int32_t local_num_points = (int32_t) num_points; -#else - uint32_t i; - uint32_t local_num_points = num_points; -#endif - Node_${DTYPE} *root = (Node_${DTYPE} *)tree->root; - - /* Queries are OpenMP enabled */ - #pragma omp parallel - { - /* The low chunk size is important to avoid L2 cache trashing - for spatial coherent query datasets - */ - #pragma omp for private(i, j) schedule(static, 100) nowait - for (i = 0; i < local_num_points; i++) - { - for (j = 0; j < k; j++) - { - closest_idxs[i * k + j] = UINT32_MAX; - closest_dists[i * k + j] = DBL_MAX; - } - min_dist = get_min_dist_${DTYPE}(point_coords + no_dims * i, no_dims, bbox); - search_splitnode_${DTYPE}(root, pa, pidx, no_dims, point_coords + no_dims * i, min_dist, - k, distance_upper_bound, eps_fac, mask, &closest_idxs[i * k], &closest_dists[i * k]); - } - } -} -% endfor diff --git a/src/utils/libkdtree/pykdtree/kdtree.c b/src/utils/libkdtree/pykdtree/kdtree.c deleted file mode 100644 index 895c0d2..0000000 --- a/src/utils/libkdtree/pykdtree/kdtree.c +++ /dev/null @@ -1,11350 +0,0 @@ -/* Generated by Cython 0.27.3 */ - -#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 < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.6+ or Python 3.3+. -#else -#define CYTHON_ABI "0_27_3" -#define CYTHON_FUTURE_DIVISION 0 -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - 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#endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #ifndef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 1 - #endif - #ifndef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000) - #endif - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #undef SHIFT - #undef BASE - #undef MASK -#endif -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define Py_OptimizeFlag 0 -#endif -#define __PYX_BUILD_PY_SSIZE_T "n" -#define CYTHON_FORMAT_SSIZE_T "z" -#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+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) - #define __Pyx_DefaultClassType PyClass_Type -#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) - #define __Pyx_DefaultClassType PyType_Type -#endif -#ifndef Py_TPFLAGS_CHECKTYPES - #define Py_TPFLAGS_CHECKTYPES 0 -#endif -#ifndef Py_TPFLAGS_HAVE_INDEX - #define Py_TPFLAGS_HAVE_INDEX 0 -#endif -#ifndef Py_TPFLAGS_HAVE_NEWBUFFER - #define Py_TPFLAGS_HAVE_NEWBUFFER 0 -#endif -#ifndef Py_TPFLAGS_HAVE_FINALIZE - #define Py_TPFLAGS_HAVE_FINALIZE 0 -#endif -#if PY_VERSION_HEX < 0x030700A0 || !defined(METH_FASTCALL) - #ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - #endif - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, Py_ssize_t nargs); - typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject **args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast - #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords -#endif -#if CYTHON_FAST_PYCCALL -#define __Pyx_PyFastCFunction_Check(func)\ - ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))))) -#else -#define __Pyx_PyFastCFunction_Check(func) 0 -#endif -#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#elif PY_VERSION_HEX >= 0x03060000 - #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() -#elif PY_VERSION_HEX >= 0x03000000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#else - #define __Pyx_PyThreadState_Current _PyThreadState_Current -#endif -#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) -#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? 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- default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); 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- #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); - return 0; -} - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyErrFetchRestore */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -} -#endif - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* GetModuleGlobalName */ -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -/* PyCFunctionFastCall */ - #if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - int flags = PyCFunction_GET_FLAGS(func); - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS))); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { - return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL); - } else { - return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs); - } -} -#endif - -/* PyFunctionFastCall */ - #if CYTHON_FAST_PYCALL -#include "frameobject.h" -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = f->f_localsplus; - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif -#endif - -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif - if (likely(PyCFunction_Check(func))) { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -/* PyObjectCallNoArg */ - #if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, NULL, 0); - } -#endif -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL); -} -#endif - -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -/* IsLittleEndian */ - static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) -{ - union { - uint32_t u32; - uint8_t u8[4]; - } S; - S.u32 = 0x01020304; - return S.u8[0] == 4; -} - -/* BufferFormatCheck */ - static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type) { - stack[0].field = &ctx->root; - stack[0].parent_offset = 0; - ctx->root.type = type; - ctx->root.name = "buffer dtype"; - ctx->root.offset = 0; - ctx->head = stack; - ctx->head->field = &ctx->root; - ctx->fmt_offset = 0; - ctx->head->parent_offset = 0; - ctx->new_packmode = '@'; - ctx->enc_packmode = '@'; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->is_complex = 0; - ctx->is_valid_array = 0; - ctx->struct_alignment = 0; - while (type->typegroup == 'S') { - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = 0; - type = type->fields->type; - } -} -static int __Pyx_BufFmt_ParseNumber(const char** ts) { - int count; - const char* t = *ts; - if (*t < '0' || *t > '9') { - return -1; - } else { - count = *t++ - '0'; - while (*t >= '0' && *t < '9') { - count *= 10; - count += *t++ - '0'; - } - } - *ts = t; - return count; -} -static int __Pyx_BufFmt_ExpectNumber(const char **ts) { - int number = __Pyx_BufFmt_ParseNumber(ts); - if (number == -1) - PyErr_Format(PyExc_ValueError,\ - "Does not understand character buffer dtype format string ('%c')", **ts); - return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* BufferGetAndValidate */ - static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (unlikely(info->buf == NULL)) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} -static void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static int __Pyx__GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - buf->buf = NULL; - if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) { - __Pyx_ZeroBuffer(buf); - return -1; - } - if (unlikely(buf->ndim != nd)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if (unlikely((unsigned)buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_SafeReleaseBuffer(buf); - return -1; -} - -/* BufferFallbackError */ - static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_SetString(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} - -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if PY_VERSION_HEX >= 0x030700A2 - *type = tstate->exc_state.exc_type; - *value = tstate->exc_state.exc_value; - *tb = tstate->exc_state.exc_traceback; - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - #endif - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if PY_VERSION_HEX >= 0x030700A2 - tmp_type = tstate->exc_state.exc_type; - tmp_value = tstate->exc_state.exc_value; - tmp_tb = tstate->exc_state.exc_traceback; - tstate->exc_state.exc_type = type; - tstate->exc_state.exc_value = value; - tstate->exc_state.exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; icurexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - if (unlikely(PyTuple_Check(err))) - return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); - return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if PY_VERSION_HEX >= 0x030700A2 - tmp_type = tstate->exc_state.exc_type; - tmp_value = tstate->exc_state.exc_value; - tmp_tb = tstate->exc_state.exc_traceback; - tstate->exc_state.exc_type = local_type; - tstate->exc_state.exc_value = local_value; - tstate->exc_state.exc_traceback = local_tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* SetupReduce */ - static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; -#else - if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; -#endif -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; - setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto GOOD; -BAD: - if (!PyErr_Occurred()) - PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); - ret = -1; -GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -/* CLineInTraceback */ - #ifndef CYTHON_CLINE_IN_TRACEBACK -static int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) { - PyObject *use_cline; - PyObject *ptype, *pvalue, *ptraceback; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject **cython_runtime_dict; -#endif - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); -#if CYTHON_COMPILING_IN_CPYTHON - cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); - if (likely(cython_runtime_dict)) { - use_cline = PyDict_GetItem(*cython_runtime_dict, __pyx_n_s_cline_in_traceback); - } else -#endif - { - PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); - if (use_cline_obj) { - use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; - Py_DECREF(use_cline_obj); - } else { - PyErr_Clear(); - use_cline = NULL; - } - } - if (!use_cline) { - c_line = 0; - PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); - } - else if (PyObject_Not(use_cline) != 0) { - c_line = 0; - } - __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); - return c_line; -} -#endif - -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -/* AddTraceback */ - #include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - if (c_line) { - c_line = __Pyx_CLineForTraceback(tstate, c_line); - } - py_code = __pyx_find_code_object(c_line ? -c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); - } - py_frame = PyFrame_New( - tstate, /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if ((0)) {} - else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); - view->obj = NULL; - Py_DECREF(obj); -} -#endif - - - /* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_uint32_t(uint32_t value) { - const uint32_t neg_one = (uint32_t) -1, const_zero = (uint32_t) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(uint32_t) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(uint32_t) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(uint32_t) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(uint32_t) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(uint32_t) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(uint32_t), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int8_t(int8_t value) { - const int8_t neg_one = (int8_t) -1, const_zero = (int8_t) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int8_t) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int8_t) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int8_t) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int8_t) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int8_t) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int8_t), - little, !is_unsigned); - } -} - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); - case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); - case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = powf(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2f(0, -1); - } - } else { - r = __Pyx_c_abs_float(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -/* Declarations */ - #if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -/* Arithmetic */ - #if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } - } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); - case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); - case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } else if (b.imag == 0) { - z.real = pow(a.real, b.real); - z.imag = 0; - return z; - } else if (a.real > 0) { - r = a.real; - theta = 0; - } else { - r = -a.real; - theta = atan2(0, -1); - } - } else { - r = __Pyx_c_abs_double(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE uint32_t __Pyx_PyInt_As_uint32_t(PyObject *x) { - const uint32_t neg_one = (uint32_t) -1, const_zero = (uint32_t) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(uint32_t) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(uint32_t, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (uint32_t) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (uint32_t) 0; - case 1: __PYX_VERIFY_RETURN_INT(uint32_t, digit, digits[0]) - case 2: - if (8 * sizeof(uint32_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) >= 2 * PyLong_SHIFT) { - return (uint32_t) (((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(uint32_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) >= 3 * PyLong_SHIFT) { - return (uint32_t) (((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(uint32_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) >= 4 * PyLong_SHIFT) { - return (uint32_t) (((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (uint32_t) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(uint32_t) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(uint32_t, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(uint32_t) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(uint32_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (uint32_t) 0; - case -1: __PYX_VERIFY_RETURN_INT(uint32_t, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(uint32_t, digit, +digits[0]) - case -2: - if (8 * sizeof(uint32_t) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) { - return (uint32_t) (((uint32_t)-1)*(((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(uint32_t) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) { - return (uint32_t) ((((((uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(uint32_t) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) { - return (uint32_t) (((uint32_t)-1)*(((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(uint32_t) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) { - return (uint32_t) ((((((((uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(uint32_t) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 4 * PyLong_SHIFT) { - return (uint32_t) (((uint32_t)-1)*(((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(uint32_t) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(uint32_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(uint32_t) - 1 > 4 * PyLong_SHIFT) { - return (uint32_t) ((((((((((uint32_t)digits[3]) << PyLong_SHIFT) | (uint32_t)digits[2]) << PyLong_SHIFT) | (uint32_t)digits[1]) << PyLong_SHIFT) | (uint32_t)digits[0]))); - } - } - break; - } -#endif - if (sizeof(uint32_t) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(uint32_t, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(uint32_t) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(uint32_t, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - uint32_t val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (uint32_t) -1; - } - } else { - uint32_t val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (uint32_t) -1; - val = __Pyx_PyInt_As_uint32_t(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to uint32_t"); - return (uint32_t) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to uint32_t"); - return (uint32_t) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* FastTypeChecks */ - #if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = a->tp_base; - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; - if (!res) { - res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } - return res; -} -#endif -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) { - if (likely(err == exc_type)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type); - } - return PyErr_GivenExceptionMatches(err, exc_type); -} -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) { - if (likely(err == exc_type1 || err == exc_type2)) return 1; - if (likely(PyExceptionClass_Check(err))) { - return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2); - } - return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2)); -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", - module_name, class_name, basicsize, size); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - if (PyObject_Hash(*t->p) == -1) - PyErr_Clear(); - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -#if !CYTHON_PEP393_ENABLED -static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { -#if PY_MAJOR_VERSION >= 3 - if (PyLong_Check(result)) { - if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, - "__int__ returned non-int (type %.200s). " - "The ability to return an instance of a strict subclass of int " - "is deprecated, and may be removed in a future version of Python.", - Py_TYPE(result)->tp_name)) { - Py_DECREF(result); - return NULL; - } - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - type_name, type_name, Py_TYPE(result)->tp_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(x); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/src/utils/libkdtree/pykdtree/kdtree.pyx b/src/utils/libkdtree/pykdtree/kdtree.pyx deleted file mode 100644 index 0f40da2..0000000 --- a/src/utils/libkdtree/pykdtree/kdtree.pyx +++ /dev/null @@ -1,280 +0,0 @@ -#pykdtree, Fast kd-tree implementation with OpenMP-enabled queries -# -#Copyright (C) 2013 - present Esben S. Nielsen -# -# This program is free software: you can redistribute it and/or modify it under -# the terms of the GNU Lesser 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 Lesser General Public License for more -# details. -# -# You should have received a copy of the GNU Lesser General Public License along -# with this program. If not, see . - -import numpy as np -cimport numpy as np -from libc.stdint cimport uint32_t, int8_t, uint8_t -cimport cython - - -# Node structure -cdef struct node_float: - float cut_val - int8_t cut_dim - uint32_t start_idx - uint32_t n - float cut_bounds_lv - float cut_bounds_hv - node_float *left_child - node_float *right_child - -cdef struct tree_float: - float *bbox - int8_t no_dims - uint32_t *pidx - node_float *root - -cdef struct node_double: - double cut_val - int8_t cut_dim - uint32_t start_idx - uint32_t n - double cut_bounds_lv - double cut_bounds_hv - node_double *left_child - node_double *right_child - -cdef struct tree_double: - double *bbox - int8_t no_dims - uint32_t *pidx - node_double *root - -cdef extern tree_float* construct_tree_float(float *pa, int8_t no_dims, uint32_t n, uint32_t bsp) nogil -cdef extern void search_tree_float(tree_float *kdtree, float *pa, float *point_coords, uint32_t num_points, uint32_t k, float distance_upper_bound, float eps_fac, uint8_t *mask, uint32_t *closest_idxs, float *closest_dists) nogil -cdef extern void delete_tree_float(tree_float *kdtree) - -cdef extern tree_double* construct_tree_double(double *pa, int8_t no_dims, uint32_t n, uint32_t bsp) nogil -cdef extern void search_tree_double(tree_double *kdtree, double *pa, double *point_coords, uint32_t num_points, uint32_t k, double distance_upper_bound, double eps_fac, uint8_t *mask, uint32_t *closest_idxs, double *closest_dists) nogil -cdef extern void delete_tree_double(tree_double *kdtree) - -cdef class KDTree: - """kd-tree for fast nearest-neighbour lookup. - The interface is made to resemble the scipy.spatial kd-tree except - only Euclidean distance measure is supported. - - :Parameters: - data_pts : numpy array - Data points with shape (n , dims) - leafsize : int, optional - Maximum number of data points in tree leaf - """ - - cdef tree_float *_kdtree_float - cdef tree_double *_kdtree_double - cdef readonly np.ndarray data_pts - cdef readonly np.ndarray data - cdef float *_data_pts_data_float - cdef double *_data_pts_data_double - cdef readonly uint32_t n - cdef readonly int8_t ndim - cdef readonly uint32_t leafsize - - def __cinit__(KDTree self): - self._kdtree_float = NULL - self._kdtree_double = NULL - - def __init__(KDTree self, np.ndarray data_pts not None, int leafsize=16): - - # Check arguments - if leafsize < 1: - raise ValueError('leafsize must be greater than zero') - - # Get data content - cdef np.ndarray[float, ndim=1] data_array_float - cdef np.ndarray[double, ndim=1] data_array_double - - if data_pts.dtype == np.float32: - data_array_float = np.ascontiguousarray(data_pts.ravel(), dtype=np.float32) - self._data_pts_data_float = data_array_float.data - self.data_pts = data_array_float - else: - data_array_double = np.ascontiguousarray(data_pts.ravel(), dtype=np.float64) - self._data_pts_data_double = data_array_double.data - self.data_pts = data_array_double - - # scipy interface compatibility - self.data = self.data_pts - - # Get tree info - self.n = data_pts.shape[0] - self.leafsize = leafsize - if data_pts.ndim == 1: - self.ndim = 1 - else: - self.ndim = data_pts.shape[1] - - # Release GIL and construct tree - if data_pts.dtype == np.float32: - with nogil: - self._kdtree_float = construct_tree_float(self._data_pts_data_float, self.ndim, - self.n, self.leafsize) - else: - with nogil: - self._kdtree_double = construct_tree_double(self._data_pts_data_double, self.ndim, - self.n, self.leafsize) - - - def query(KDTree self, np.ndarray query_pts not None, k=1, eps=0, - distance_upper_bound=None, sqr_dists=False, mask=None): - """Query the kd-tree for nearest neighbors - - :Parameters: - query_pts : numpy array - Query points with shape (m, dims) - k : int - The number of nearest neighbours to return - eps : non-negative float - Return approximate nearest neighbours; the k-th returned value - is guaranteed to be no further than (1 + eps) times the distance - to the real k-th nearest neighbour - distance_upper_bound : non-negative float - Return only neighbors within this distance. - This is used to prune tree searches. - sqr_dists : bool, optional - Internally pykdtree works with squared distances. - Determines if the squared or Euclidean distances are returned. - mask : numpy array, optional - Array of booleans where neighbors are considered invalid and - should not be returned. A mask value of True represents an - invalid pixel. Mask should have shape (n,) to match data points. - By default all points are considered valid. - - """ - - # Check arguments - if k < 1: - raise ValueError('Number of neighbours must be greater than zero') - elif eps < 0: - raise ValueError('eps must be non-negative') - elif distance_upper_bound is not None: - if distance_upper_bound < 0: - raise ValueError('distance_upper_bound must be non negative') - - # Check dimensions - if query_pts.ndim == 1: - q_ndim = 1 - else: - q_ndim = query_pts.shape[1] - - if self.ndim != q_ndim: - raise ValueError('Data and query points must have same dimensions') - - if self.data_pts.dtype == np.float32 and query_pts.dtype != np.float32: - raise TypeError('Type mismatch. query points must be of type float32 when data points are of type float32') - - # Get query info - cdef uint32_t num_qpoints = query_pts.shape[0] - cdef uint32_t num_n = k - cdef np.ndarray[uint32_t, ndim=1] closest_idxs = np.empty(num_qpoints * k, dtype=np.uint32) - cdef np.ndarray[float, ndim=1] closest_dists_float - cdef np.ndarray[double, ndim=1] closest_dists_double - - - # Set up return arrays - cdef uint32_t *closest_idxs_data = closest_idxs.data - cdef float *closest_dists_data_float - cdef double *closest_dists_data_double - - # Get query points data - cdef np.ndarray[float, ndim=1] query_array_float - cdef np.ndarray[double, ndim=1] query_array_double - cdef float *query_array_data_float - cdef double *query_array_data_double - cdef np.ndarray[np.uint8_t, ndim=1] query_mask - cdef np.uint8_t *query_mask_data - - if mask is not None and mask.size != self.n: - raise ValueError('Mask must have the same size as data points') - elif mask is not None: - query_mask = np.ascontiguousarray(mask.ravel(), dtype=np.uint8) - query_mask_data = query_mask.data - else: - query_mask_data = NULL - - - if query_pts.dtype == np.float32 and self.data_pts.dtype == np.float32: - closest_dists_float = np.empty(num_qpoints * k, dtype=np.float32) - closest_dists = closest_dists_float - closest_dists_data_float = closest_dists_float.data - query_array_float = np.ascontiguousarray(query_pts.ravel(), dtype=np.float32) - query_array_data_float = query_array_float.data - else: - closest_dists_double = np.empty(num_qpoints * k, dtype=np.float64) - closest_dists = closest_dists_double - closest_dists_data_double = closest_dists_double.data - query_array_double = np.ascontiguousarray(query_pts.ravel(), dtype=np.float64) - query_array_data_double = query_array_double.data - - # Setup distance_upper_bound - cdef float dub_float - cdef double dub_double - if distance_upper_bound is None: - if self.data_pts.dtype == np.float32: - dub_float = np.finfo(np.float32).max - else: - dub_double = np.finfo(np.float64).max - else: - if self.data_pts.dtype == np.float32: - dub_float = (distance_upper_bound * distance_upper_bound) - else: - dub_double = (distance_upper_bound * distance_upper_bound) - - # Set epsilon - cdef double epsilon_float = eps - cdef double epsilon_double = eps - - # Release GIL and query tree - if self.data_pts.dtype == np.float32: - with nogil: - search_tree_float(self._kdtree_float, self._data_pts_data_float, - query_array_data_float, num_qpoints, num_n, dub_float, epsilon_float, - query_mask_data, closest_idxs_data, closest_dists_data_float) - - else: - with nogil: - search_tree_double(self._kdtree_double, self._data_pts_data_double, - query_array_data_double, num_qpoints, num_n, dub_double, epsilon_double, - query_mask_data, closest_idxs_data, closest_dists_data_double) - - # Shape result - if k > 1: - closest_dists_res = closest_dists.reshape(num_qpoints, k) - closest_idxs_res = closest_idxs.reshape(num_qpoints, k) - else: - closest_dists_res = closest_dists - closest_idxs_res = closest_idxs - - if distance_upper_bound is not None: # Mark out of bounds results - if self.data_pts.dtype == np.float32: - idx_out = (closest_dists_res >= dub_float) - else: - idx_out = (closest_dists_res >= dub_double) - - closest_dists_res[idx_out] = np.Inf - closest_idxs_res[idx_out] = self.n - - if not sqr_dists: # Return actual cartesian distances - closest_dists_res = np.sqrt(closest_dists_res) - - return closest_dists_res, closest_idxs_res - - def __dealloc__(KDTree self): - if self._kdtree_float != NULL: - delete_tree_float(self._kdtree_float) - elif self._kdtree_double != NULL: - delete_tree_double(self._kdtree_double) diff --git a/src/utils/libkdtree/pykdtree/render_template.py b/src/utils/libkdtree/pykdtree/render_template.py deleted file mode 100644 index 34cc167..0000000 --- a/src/utils/libkdtree/pykdtree/render_template.py +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env python - -from mako.template import Template - -mytemplate = Template(filename='_kdtree_core.c.mako') -with open('_kdtree_core.c', 'w') as fp: - fp.write(mytemplate.render()) diff --git a/src/utils/libkdtree/pykdtree/test_tree.py b/src/utils/libkdtree/pykdtree/test_tree.py deleted file mode 100644 index 5b33b74..0000000 --- a/src/utils/libkdtree/pykdtree/test_tree.py +++ /dev/null @@ -1,353 +0,0 @@ -import numpy as np - -from pykdtree.kdtree import KDTree - - -data_pts_real = np.array([[ 790535.062, -369324.656, 6310963.5 ], - [ 790024.312, -365155.688, 6311270. ], - [ 789515.75 , -361009.469, 6311572. ], - [ 789011. , -356886.562, 6311869.5 ], - [ 788508.438, -352785.969, 6312163. ], - [ 788007.25 , -348707.219, 6312452. ], - [ 787509.188, -344650.875, 6312737. ], - [ 787014.438, -340616.906, 6313018. ], - [ 786520.312, -336604.156, 6313294.5 ], - [ 786030.312, -332613.844, 6313567. ], - [ 785541.562, -328644.375, 6313835.5 ], - [ 785054.75 , -324696.031, 6314100.5 ], - [ 784571.188, -320769.5 , 6314361.5 ], - [ 784089.312, -316863.562, 6314618.5 ], - [ 783610.562, -312978.719, 6314871.5 ], - [ 783133. , -309114.312, 6315121. ], - [ 782658.25 , -305270.531, 6315367. ], - [ 782184.312, -301446.719, 6315609. ], - [ 781715.062, -297643.844, 6315847.5 ], - [ 781246.188, -293860.281, 6316083. ], - [ 780780.125, -290096.938, 6316314.5 ], - [ 780316.312, -286353.469, 6316542.5 ], - [ 779855.625, -282629.75 , 6316767.5 ], - [ 779394.75 , -278924.781, 6316988.5 ], - [ 778937.312, -275239.625, 6317206.5 ], - [ 778489.812, -271638.094, 6317418. ], - [ 778044.688, -268050.562, 6317626. ], - [ 777599.688, -264476.75 , 6317831.5 ], - [ 777157.625, -260916.859, 6318034. ], - [ 776716.688, -257371.125, 6318233.5 ], - [ 776276.812, -253838.891, 6318430.5 ], - [ 775838.125, -250320.266, 6318624.5 ], - [ 775400.75 , -246815.516, 6318816.5 ], - [ 774965.312, -243324.953, 6319005. ], - [ 774532.062, -239848.25 , 6319191. ], - [ 774100.25 , -236385.516, 6319374.5 ], - [ 773667.875, -232936.016, 6319555.5 ], - [ 773238.562, -229500.812, 6319734. ], - [ 772810.938, -226079.562, 6319909.5 ], - [ 772385.25 , -222672.219, 6320082.5 ], - [ 771960. , -219278.5 , 6320253. ], - [ 771535.938, -215898.609, 6320421. ], - [ 771114. , -212532.625, 6320587. ], - [ 770695. , -209180.859, 6320749.5 ], - [ 770275.25 , -205842.562, 6320910.5 ], - [ 769857.188, -202518.125, 6321068.5 ], - [ 769442.312, -199207.844, 6321224.5 ], - [ 769027.812, -195911.203, 6321378. ], - [ 768615.938, -192628.859, 6321529. ], - [ 768204.688, -189359.969, 6321677.5 ], - [ 767794.062, -186104.844, 6321824. ], - [ 767386.25 , -182864.016, 6321968.5 ], - [ 766980.062, -179636.969, 6322110. ], - [ 766575.625, -176423.75 , 6322249.5 ], - [ 766170.688, -173224.172, 6322387. ], - [ 765769.812, -170038.984, 6322522.5 ], - [ 765369.5 , -166867.312, 6322655. ], - [ 764970.562, -163709.594, 6322786. ], - [ 764573. , -160565.781, 6322914.5 ], - [ 764177.75 , -157435.938, 6323041. ], - [ 763784.188, -154320.062, 6323165.5 ], - [ 763392.375, -151218.047, 6323288. ], - [ 763000.938, -148129.734, 6323408. ], - [ 762610.812, -145055.344, 6323526.5 ], - [ 762224.188, -141995.141, 6323642.5 ], - [ 761847.188, -139025.734, 6323754. ], - [ 761472.375, -136066.312, 6323863.5 ], - [ 761098.125, -133116.859, 6323971.5 ], - [ 760725.25 , -130177.484, 6324077.5 ], - [ 760354. , -127247.984, 6324181.5 ], - [ 759982.812, -124328.336, 6324284.5 ], - [ 759614. , -121418.844, 6324385. ], - [ 759244.688, -118519.102, 6324484.5 ], - [ 758877.125, -115629.305, 6324582. ], - [ 758511.562, -112749.648, 6324677.5 ], - [ 758145.625, -109879.82 , 6324772.5 ], - [ 757781.688, -107019.953, 6324865. ], - [ 757418.438, -104170.047, 6324956. ], - [ 757056.562, -101330.125, 6325045.5 ], - [ 756697. , -98500.266, 6325133.5 ], - [ 756337.375, -95680.289, 6325219.5 ], - [ 755978.062, -92870.148, 6325304.5 ], - [ 755621.188, -90070.109, 6325387.5 ], - [ 755264.625, -87280.008, 6325469. ], - [ 754909.188, -84499.828, 6325549. ], - [ 754555.062, -81729.609, 6325628. ], - [ 754202.938, -78969.43 , 6325705. ], - [ 753850.688, -76219.133, 6325781. ], - [ 753499.875, -73478.836, 6325855. ], - [ 753151.375, -70748.578, 6325927.5 ], - [ 752802.312, -68028.188, 6325999. ], - [ 752455.75 , -65317.871, 6326068.5 ], - [ 752108.625, -62617.344, 6326137.5 ], - [ 751764.125, -59926.969, 6326204.5 ], - [ 751420.125, -57246.434, 6326270. ], - [ 751077.438, -54575.902, 6326334.5 ], - [ 750735.312, -51915.363, 6326397.5 ], - [ 750396.188, -49264.852, 6326458.5 ], - [ 750056.375, -46624.227, 6326519. ], - [ 749718.875, -43993.633, 6326578. ]]) - -def test1d(): - - data_pts = np.arange(1000) - kdtree = KDTree(data_pts, leafsize=15) - query_pts = np.arange(400, 300, -10) - dist, idx = kdtree.query(query_pts) - assert idx[0] == 400 - assert dist[0] == 0 - assert idx[1] == 390 - -def test3d(): - - - #7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, sqr_dists=True) - - epsilon = 1e-5 - assert idx[0] == 7 - assert idx[1] == 93 - assert idx[2] == 45 - assert dist[0] == 0 - assert abs(dist[1] - 3.) < epsilon * dist[1] - assert abs(dist[2] - 20001.) < epsilon * dist[2] - -def test3d_float32(): - - - #7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]], dtype=np.float32) - - - kdtree = KDTree(data_pts_real.astype(np.float32)) - dist, idx = kdtree.query(query_pts, sqr_dists=True) - epsilon = 1e-5 - assert idx[0] == 7 - assert idx[1] == 93 - assert idx[2] == 45 - assert dist[0] == 0 - assert abs(dist[1] - 3.) < epsilon * dist[1] - assert abs(dist[2] - 20001.) < epsilon * dist[2] - assert kdtree.data_pts.dtype == np.float32 - -def test3d_float32_mismatch(): - - - #7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]], dtype=np.float32) - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, sqr_dists=True) - -def test3d_float32_mismatch2(): - - - #7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real.astype(np.float32)) - try: - dist, idx = kdtree.query(query_pts, sqr_dists=True) - assert False - except TypeError: - assert True - - -def test3d_8n(): - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, k=8) - - exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03, - 8.17063009e+03, 1.20904577e+04, 1.22902057e+04, 1.60775136e+04], - [ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03, - 5.43793210e+03, 8.07855631e+03, 8.17119970e+03, 1.07513693e+04], - [ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03, - 6.81038455e+03, 9.89140135e+03, 1.01918659e+04, 1.31892516e+04]]) - - exp_idx = np.array([[ 7, 8, 6, 9, 5, 10, 4, 11], - [93, 94, 92, 95, 91, 96, 90, 97], - [45, 46, 44, 47, 43, 48, 42, 49]]) - - assert np.array_equal(idx, exp_idx) - assert np.allclose(dist, exp_dist) - -def test3d_8n_ub(): - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, k=8, distance_upper_bound=10e3, sqr_dists=False) - - exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03, - 8.17063009e+03, np.Inf, np.Inf, np.Inf], - [ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03, - 5.43793210e+03, 8.07855631e+03, 8.17119970e+03, np.Inf], - [ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03, - 6.81038455e+03, 9.89140135e+03, np.Inf, np.Inf]]) - n = 100 - exp_idx = np.array([[ 7, 8, 6, 9, 5, n, n, n], - [93, 94, 92, 95, 91, 96, 90, n], - [45, 46, 44, 47, 43, 48, n, n]]) - - assert np.array_equal(idx, exp_idx) - assert np.allclose(dist, exp_dist) - -def test3d_8n_ub_leaf20(): - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real, leafsize=20) - dist, idx = kdtree.query(query_pts, k=8, distance_upper_bound=10e3, sqr_dists=False) - - exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03, - 8.17063009e+03, np.Inf, np.Inf, np.Inf], - [ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03, - 5.43793210e+03, 8.07855631e+03, 8.17119970e+03, np.Inf], - [ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03, - 6.81038455e+03, 9.89140135e+03, np.Inf, np.Inf]]) - n = 100 - exp_idx = np.array([[ 7, 8, 6, 9, 5, n, n, n], - [93, 94, 92, 95, 91, 96, 90, n], - [45, 46, 44, 47, 43, 48, n, n]]) - - assert np.array_equal(idx, exp_idx) - assert np.allclose(dist, exp_dist) - -def test3d_8n_ub_eps(): - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, k=8, eps=0.1, distance_upper_bound=10e3, sqr_dists=False) - - exp_dist = np.array([[ 0.00000000e+00, 4.05250235e+03, 4.07389794e+03, 8.08201128e+03, - 8.17063009e+03, np.Inf, np.Inf, np.Inf], - [ 1.73205081e+00, 2.70216896e+03, 2.71431274e+03, 5.39537066e+03, - 5.43793210e+03, 8.07855631e+03, 8.17119970e+03, np.Inf], - [ 1.41424892e+02, 3.25500021e+03, 3.44284958e+03, 6.58019346e+03, - 6.81038455e+03, 9.89140135e+03, np.Inf, np.Inf]]) - n = 100 - exp_idx = np.array([[ 7, 8, 6, 9, 5, n, n, n], - [93, 94, 92, 95, 91, 96, 90, n], - [45, 46, 44, 47, 43, 48, n, n]]) - - assert np.array_equal(idx, exp_idx) - assert np.allclose(dist, exp_dist) - -def test3d_large_query(): - # Target idxs: 7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - # Repeat the same points multiple times to get 60000 query points - n = 20000 - query_pts = np.repeat(query_pts, n, axis=0) - - kdtree = KDTree(data_pts_real) - dist, idx = kdtree.query(query_pts, sqr_dists=True) - - epsilon = 1e-5 - assert np.all(idx[:n] == 7) - assert np.all(idx[n:2*n] == 93) - assert np.all(idx[2*n:] == 45) - assert np.all(dist[:n] == 0) - assert np.all(abs(dist[n:2*n] - 3.) < epsilon * dist[n:2*n]) - assert np.all(abs(dist[2*n:] - 20001.) < epsilon * dist[2*n:]) - -def test_scipy_comp(): - - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real) - assert id(kdtree.data) == id(kdtree.data_pts) - - -def test1d_mask(): - data_pts = np.arange(1000) - # put the input locations in random order - np.random.shuffle(data_pts) - bad_idx = np.nonzero(data_pts == 400) - nearest_idx_1 = np.nonzero(data_pts == 399) - nearest_idx_2 = np.nonzero(data_pts == 390) - kdtree = KDTree(data_pts, leafsize=15) - # shift the query points just a little bit for known neighbors - # we want 399 as a result, not 401, when we query for ~400 - query_pts = np.arange(399.9, 299.9, -10) - query_mask = np.zeros(data_pts.shape[0]).astype(bool) - query_mask[bad_idx] = True - dist, idx = kdtree.query(query_pts, mask=query_mask) - assert idx[0] == nearest_idx_1 # 399, would be 400 if no mask - assert np.isclose(dist[0], 0.9) - assert idx[1] == nearest_idx_2 # 390 - assert np.isclose(dist[1], 0.1) - - -def test1d_all_masked(): - data_pts = np.arange(1000) - np.random.shuffle(data_pts) - kdtree = KDTree(data_pts, leafsize=15) - query_pts = np.arange(400, 300, -10) - query_mask = np.ones(data_pts.shape[0]).astype(bool) - dist, idx = kdtree.query(query_pts, mask=query_mask) - # all invalid - assert np.all(i >= 1000 for i in idx) - assert np.all(d >= 1001 for d in dist) - - -def test3d_mask(): - #7, 93, 45 - query_pts = np.array([[ 787014.438, -340616.906, 6313018.], - [751763.125, -59925.969, 6326205.5], - [769957.188, -202418.125, 6321069.5]]) - - kdtree = KDTree(data_pts_real) - query_mask = np.zeros(data_pts_real.shape[0]) - query_mask[6:10] = True - dist, idx = kdtree.query(query_pts, sqr_dists=True, mask=query_mask) - - epsilon = 1e-5 - assert idx[0] == 5 # would be 7 if no mask - assert idx[1] == 93 - assert idx[2] == 45 - # would be 0 if no mask - assert abs(dist[0] - 66759196.1053) < epsilon * dist[0] - assert abs(dist[1] - 3.) < epsilon * dist[1] - assert abs(dist[2] - 20001.) < epsilon * dist[2] diff --git a/src/utils/libkdtree/setup.cfg b/src/utils/libkdtree/setup.cfg deleted file mode 100644 index c595009..0000000 --- a/src/utils/libkdtree/setup.cfg +++ /dev/null @@ -1,5 +0,0 @@ -[bdist_rpm] -requires=numpy -release=1 - - From 94e8ee1bff32a3904f8077c978ca9a10acd5e589 Mon Sep 17 00:00:00 2001 From: Alvin Sun Date: Thu, 6 Apr 2023 14:12:28 -0700 Subject: [PATCH 2/4] making train.py run --- configs/pointcloud/shapenet_pointconv.yaml | 2 +- src/data/core.py | 35 ++++++++++++---------- 2 files changed, 21 insertions(+), 16 deletions(-) diff --git a/configs/pointcloud/shapenet_pointconv.yaml b/configs/pointcloud/shapenet_pointconv.yaml index adf5e81..ec780c7 100644 --- a/configs/pointcloud/shapenet_pointconv.yaml +++ b/configs/pointcloud/shapenet_pointconv.yaml @@ -2,7 +2,7 @@ method: conv_onet data: input_type: pointcloud classes: null - path: data/ShapeNet + path: /mnt/data/ShapeNet pointcloud_n: 3000 pointcloud_noise: 0.005 points_subsample: 2048 diff --git a/src/data/core.py b/src/data/core.py index bcd3747..4b16659 100644 --- a/src/data/core.py +++ b/src/data/core.py @@ -3,6 +3,11 @@ from torch.utils import data import numpy as np import yaml +try: + from yaml import CLoader as Loader +except: + from yaml import Loader + from src.common import decide_total_volume_range, update_reso @@ -68,12 +73,12 @@ def __init__(self, dataset_folder, fields, split=None, if os.path.exists(metadata_file): with open(metadata_file, 'r') as f: - self.metadata = yaml.load(f) + self.metadata = yaml.load(f, Loader=Loader) else: self.metadata = { c: {'id': c, 'name': 'n/a'} for c in categories - } - + } + # Set index for c_idx, c in enumerate(categories): self.metadata[c]['idx'] = c_idx @@ -94,7 +99,7 @@ def __init__(self, dataset_folder, fields, split=None, split_file = os.path.join(subpath, split + '.lst') with open(split_file, 'r') as f: models_c = f.read().split('\n') - + if '' in models_c: models_c.remove('') @@ -102,9 +107,9 @@ def __init__(self, dataset_folder, fields, split=None, {'category': c, 'model': m} for m in models_c ] - + # precompute - if self.cfg['data']['input_type'] == 'pointcloud_crop': + if self.cfg['data']['input_type'] == 'pointcloud_crop': self.split = split # proper resolution for feature plane/volume of the ENTIRE scene query_vol_metric = self.cfg['data']['padding'] + 1 @@ -114,7 +119,7 @@ def __init__(self, dataset_folder, fields, split=None, depth = cfg['model']['encoder_kwargs']['unet_kwargs']['depth'] elif 'unet3d' in cfg['model']['encoder_kwargs']: depth = cfg['model']['encoder_kwargs']['unet3d_kwargs']['num_levels'] - + self.depth = depth #! for sliding-window case, pass all points! if self.cfg['generation']['sliding_window']: @@ -124,7 +129,7 @@ def __init__(self, dataset_folder, fields, split=None, self.total_input_vol, self.total_query_vol, self.total_reso = \ decide_total_volume_range(query_vol_metric, recep_field, unit_size, depth) - + def __len__(self): ''' Returns the length of the dataset. ''' @@ -148,7 +153,7 @@ def __getitem__(self, idx): data['pointcloud_crop'] = True else: info = c_idx - + for field_name, field in self.fields.items(): try: field_data = field.load(model_path, idx, info) @@ -175,7 +180,7 @@ def __getitem__(self, idx): data = self.transform(data) return data - + def get_vol_info(self, model_path): ''' Get crop information @@ -193,7 +198,7 @@ def get_vol_info(self, model_path): else: num = np.random.randint(self.cfg['data']['multi_files']) file_path = os.path.join(model_path, field_name, '%s_%02d.npz' % (field_name, num)) - + points_dict = np.load(file_path) p = points_dict['points'] if self.split == 'train': @@ -201,7 +206,7 @@ def get_vol_info(self, model_path): p_c = [np.random.uniform(p[:,i].min(), p[:,i].max()) for i in range(3)] # p_c = [np.random.uniform(-0.55, 0.55) for i in range(3)] p_c = np.array(p_c).astype(np.float32) - + reso = query_vol_size + recep_field - 1 # make sure the defined reso can be properly processed by UNet reso = update_reso(reso, self.depth) @@ -224,7 +229,7 @@ def get_vol_info(self, model_path): 'input_vol' : input_vol, 'query_vol' : query_vol} return vol_info - + def get_model_dict(self, idx): return self.models[idx] @@ -261,9 +266,9 @@ def worker_init_fn(worker_id): ''' Worker init function to ensure true randomness. ''' def set_num_threads(nt): - try: + try: import mkl; mkl.set_num_threads(nt) - except: + except: pass torch.set_num_threads(1) os.environ['IPC_ENABLE']='1' From a771c3d256ab1f467e9649ed58e094f8bc33fc30 Mon Sep 17 00:00:00 2001 From: Alvin Sun Date: Thu, 6 Apr 2023 14:37:58 -0700 Subject: [PATCH 3/4] update environment --- environment.yaml | 45 +++++++++++++++++++++------------------------ 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/environment.yaml b/environment.yaml index bfc9f10..591a86f 100644 --- a/environment.yaml +++ b/environment.yaml @@ -4,27 +4,24 @@ channels: - pytorch - defaults dependencies: - - cython=0.29.2 - - imageio=2.4.1 - - numpy=1.15.4 - - numpy-base=1.15.4 - - matplotlib=3.0.3 - - matplotlib-base=3.0.3 - - pandas=0.23.4 - - pillow=5.3.0 - - pyembree=0.1.4 - - pytest=4.0.2 - - python=3.6.7 - - pytorch=1.4.0 - - pyyaml=3.13 - - scikit-image=0.14.1 - - scipy=1.1.0 - - tensorboardx=1.4 - - torchvision=0.2.1 - - tqdm=4.28.1 - - trimesh=2.37.7 - - pip: - - h5py==2.9.0 - - plyfile==0.7 - - torch_scatter==2.0.4 - + - cython=0.29.34 + - h5py=3.8.0 + - imageio=2.27.0 + - numpy=1.24.2 + - matplotlib=3.7.1 + - pandas=2.0.0 + - pillow=9.4.0 + - plyfile=0.8.1 + - pyembree=0.1.6 + - pykdtree=1.3.7.post0 + - pytest=7.2.2 + - python=3.10 + - pytorch=2.0.0 + - pytorch-scatter=2.1.1 + - pyyaml=6.0 + - scikit-image=0.20.0 + - scipy=1.10.1 + - tensorboardx=2.5.1 + - torchvision=0.15.0 + - tqdm=4.65.0 + - trimesh=3.21.4 From fb3a91ff75f0d50c47efeefcbfdb1d8952f63d6e Mon Sep 17 00:00:00 2001 From: Alvin Sun Date: Thu, 6 Apr 2023 20:47:05 -0700 Subject: [PATCH 4/4] update readme and revert config --- README.md | 4 ---- configs/pointcloud/shapenet_pointconv.yaml | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index f93a796..9377b50 100644 --- a/README.md +++ b/README.md @@ -31,10 +31,6 @@ You can create an anaconda environment called `conv_onet` using conda env create -f environment.yaml conda activate conv_onet ``` -**Note**: you might need to install **torch-scatter** mannually following [the official instruction](https://github.com/rusty1s/pytorch_scatter#pytorch-140): -``` -pip install torch-scatter==2.0.4 -f https://pytorch-geometric.com/whl/torch-1.4.0+cu101.html -``` Next, compile the extension modules. You can do this via diff --git a/configs/pointcloud/shapenet_pointconv.yaml b/configs/pointcloud/shapenet_pointconv.yaml index ec780c7..adf5e81 100644 --- a/configs/pointcloud/shapenet_pointconv.yaml +++ b/configs/pointcloud/shapenet_pointconv.yaml @@ -2,7 +2,7 @@ method: conv_onet data: input_type: pointcloud classes: null - path: /mnt/data/ShapeNet + path: data/ShapeNet pointcloud_n: 3000 pointcloud_noise: 0.005 points_subsample: 2048