def __init__(self, pts, max_depth=None):
if not len(pts.shape) == 2 and not pts.shape[1] == 4:
raise ValueError('pts must be a numpy ndarray with shape (length,4).')
if max_depth == None:
max_depth = math.log(len(pts), 2) + 2
self._points = CLReal()
parr = np.array(pts).flatten()
parr.shape = (parr.shape[0], 1)
self._points.value = parr
data = list(kd_c.get_tree(pts))
for i in range(len(data)):
data[i] = data[i].flatten()
data[i].shape = (data[i].shape[0], 1)
self._id = CLInt()
self._parent = CLInt()
self._cdim = CLInt()
self._pt = CLInt()
self._lo = CLInt()
self._hi = CLInt()
self._vmax = CLReal()
self._vmin = CLReal()
self._cval = CLReal()
self._offset = CLScalar(0)
self._id.value = data[0]
self._parent.value = data[1]
self._cdim.value = data[2]
self._pt.value = data[3]
self._lo.value = data[4]
self._hi.value = data[5]
self._vmin.value = data[6]
self._vmax.value = data[7]
self._cval.value = data[8]
#prepare for nns
self._query_points = CLReal4()
self._neigh_i = CLInt()
self._neigh_d = CLReal()
#prepare the kernel
pre = "#define MAX_DEPTH " + str(int(max_depth))
self._get_nn_krn = CLTemplateKernel(pre=pre, src=_GET_NN_SRC)
self._get_nn_krn.query_points = self._query_points
self._get_nn_krn.points = self._points
self._get_nn_krn.id = self._id
self._get_nn_krn.parent = self._parent
self._get_nn_krn.cdim = self._cdim
self._get_nn_krn.pt = self._pt
self._get_nn_krn.lo = self._lo
self._get_nn_krn.hi = self._hi
self._get_nn_krn.vmin = self._vmin
self._get_nn_krn.vmax = self._vmax
self._get_nn_krn.cval = self._cval
self._get_nn_krn.neigh_i = self._neigh_i
self._get_nn_krn.neigh_d = self._neigh_d
self._get_nn_krn.offset = self._offset
self._get_nn_krn.compile()
# -*- coding: utf-8 -*-
"""
Created on Sun Dec 6 17:29:12 2015
@author: Hagen
"""
import kd_c
import numpy as np
length = 10000
points = np.random.rand(length,4)
#print points
tup = kd_c.get_tree(points)
c = 0;
for i in tup:
print len(i), i
