def _test(nr_points, dim, bucket_size, radius):
"""Test neighbor search.
Test neighbor search using the KD tree C module.
o nr_points - number of points used in test
o dim - dimension of coords
o bucket_size - nr of points per tree node
o radius - radius of search (typically 0.05 or so)
"""
# kd tree search
kdt = _CKDTree.KDTree(dim, bucket_size)
coords = random((nr_points, dim))
center = coords[0]
kdt.set_data(coords)
kdt.search_center_radius(center, radius)
r = kdt.get_indices()
if r is None:
l1 = 0
else:
l1 = len(r)
l2 = 0
# now do a manual search to compare results
for i in range(0, nr_points):
p = coords[i]
if _dist(p, center) <= radius:
l2 = l2 + 1
if l1 == l2:
print "Passed."
else:
print "Not passed: %i != %i." % (l1, l2)
def _neighbor_test(nr_points, dim, bucket_size, radius):
""" Test all fixed radius neighbor search.
Test all fixed radius neighbor search using the
KD tree C module.
o nr_points - number of points used in test
o dim - dimension of coords
o bucket_size - nr of points per tree node
o radius - radius of search (typically 0.05 or so)
"""
# KD tree search
kdt = _CKDTree.KDTree(dim, bucket_size)
coords = random((nr_points, dim))
kdt.set_data(coords)
neighbors = kdt.neighbor_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l1 = 0
else:
l1 = len(r)
# now do a slow search to compare results
neighbors = kdt.neighbor_simple_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l2 = 0
else:
l2 = len(r)
if l1 == l2:
print "Passed."
else:
print "Not passed: %i != %i." % (l1, l2)
def __init__(self, box, bucket_size=10):
"""
Parameters
----------
box : array-like or ``None``, optional, default ``None``
Simulation cell dimensions in the form of
:attr:`MDAnalysis.trajectory.base.Timestep.dimensions` when
periodic boundary conditions should be taken into account for
the calculation of contacts.
bucket_size : int
Number of entries in leafs of the KDTree. If you suffer poor
performance you can play around with this number. Increasing the
`bucket_size` will speed up the construction of the KDTree but
slow down the search.
"""
self.dim = 3 # 3D systems
self.box = None
self._dm = None # matrix of central-cell vectors
self._rm = None # matrix of normalized reciprocal vectors
self.initialize_bm(box)
self.kdt = _CKDTree.KDTree(self.dim, bucket_size)
self._indices = list()
def _neighbor_test(nr_points, dim, bucket_size, radius):
"""Test all fixed radius neighbor search.
Test all fixed radius neighbor search using the
KD tree C module.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- radius: radius of search (typically 0.05 or so)
Returns true if the test passes.
"""
# KD tree search
kdt = _CKDTree.KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.set_data(coords)
neighbors = kdt.neighbor_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l1 = 0
else:
l1 = len(r)
# now do a slow search to compare results
neighbors = kdt.neighbor_simple_search(radius)
r = [neighbor.radius for neighbor in neighbors]
if r is None:
l2 = 0
else:
l2 = len(r)
if l1 == l2:
# print("Passed.")
return True
else:
print("Not passed: %i != %i." % (l1, l2))
return False
def _test(nr_points, dim, bucket_size, radius):
"""Test neighbor search.
Test neighbor search using the KD tree C module.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- radius: radius of search (typically 0.05 or so)
Returns true if the test passes.
"""
# kd tree search
kdt = _CKDTree.KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
center = coords[0]
kdt.set_data(coords)
kdt.search_center_radius(center, radius)
r = kdt.get_indices()
if r is None:
l1 = 0
else:
l1 = len(r)
l2 = 0
# now do a manual search to compare results
for i in range(0, nr_points):
p = coords[i]
if _dist(p, center) <= radius:
l2 = l2 + 1
if l1 == l2:
# print("Passed.")
return True
else:
print("Not passed: %i != %i." % (l1, l2))
return False
def __init__(self, dim, bucket_size=1):
"""Initialize KDTree class."""
self.dim = dim
self.kdt = _CKDTree.KDTree(dim, bucket_size)
self.built = 0
def __init__(self, dim, bucket_size=1):
self.dim = dim
self.kdt = _CKDTree.KDTree(dim, bucket_size)
self.built = 0
