def test_all_search(nr_points, dim, bucket_size, query_radius):
"""Test fixed neighbor search.
Search all point pairs that are within radius.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- query_radius: radius of search
Returns true if the test passes.
"""
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.set_coords(coords)
kdt.all_search(query_radius)
indices = kdt.all_get_indices()
if indices is None:
l1 = 0
else:
l1 = len(indices)
radii = kdt.all_get_radii()
if radii is None:
l2 = 0
else:
l2 = len(radii)
if l1 == l2:
return True
else:
return False
def test_all_search(nr_points, dim, bucket_size, query_radius):
"""Test fixed neighbor search.
Search all point pairs that are within radius.
Arguments:
- nr_points: number of points used in test
- dim: dimension of coords
- bucket_size: nr of points per tree node
- query_radius: radius of search
Returns true if the test passes.
"""
kdt = KDTree(dim, bucket_size)
coords = random.random((nr_points, dim))
kdt.set_coords(coords)
kdt.all_search(query_radius)
indices = kdt.all_get_indices()
if indices is None:
l1 = 0
else:
l1 = len(indices)
radii = kdt.all_get_radii()
if radii is None:
l2 = 0
else:
l2 = len(radii)
if l1 == l2:
return True
else:
return False
class NeighborSearch(object):
"""Class for neighbor searching,
This class can be used for two related purposes:
1. To find all atoms/residues/chains/models/structures within radius
of a given query position.
2. To find all atoms/residues/chains/models/structures that are within
a fixed radius of each other.
NeighborSearch makes use of the Bio.KDTree C++ module, so it's fast.
"""
def __init__(self, atom_list, bucket_size=10):
"""Create the object.
Arguments:
- atom_list - list of atoms. This list is used in the queries.
It can contain atoms from different structures.
- bucket_size - bucket size of KD tree. You can play around
with this to optimize speed if you feel like it.
"""
self.atom_list = atom_list
# get the coordinates
coord_list = [a.get_coord() for a in atom_list]
# to Nx3 array of type float
self.coords = numpy.array(coord_list).astype("f")
assert bucket_size > 1
assert self.coords.shape[1] == 3
self.kdt = KDTree(3, bucket_size)
self.kdt.set_coords(self.coords)
# Private
def _get_unique_parent_pairs(self, pair_list):
# translate a list of (entity, entity) tuples to
# a list of (parent entity, parent entity) tuples,
# thereby removing duplicate (parent entity, parent entity)
# pairs.
# o pair_list - a list of (entity, entity) tuples
parent_pair_list = []
for (e1, e2) in pair_list:
p1 = e1.get_parent()
p2 = e2.get_parent()
if p1 == p2:
continue
elif p1 < p2:
parent_pair_list.append((p1, p2))
else:
parent_pair_list.append((p2, p1))
return uniqueify(parent_pair_list)
# Public
def search(self, center, radius, level="A"):
"""Neighbor search.
Return all atoms/residues/chains/models/structures
that have at least one atom within radius of center.
What entity level is returned (e.g. atoms or residues)
is determined by level (A=atoms, R=residues, C=chains,
M=models, S=structures).
Arguments:
- center - Numeric array
- radius - float
- level - char (A, R, C, M, S)
"""
if level not in entity_levels:
raise PDBException("%s: Unknown level" % level)
self.kdt.search(center, radius)
indices = self.kdt.get_indices()
n_atom_list = []
atom_list = self.atom_list
for i in indices:
a = atom_list[i]
n_atom_list.append(a)
if level == "A":
return n_atom_list
else:
return unfold_entities(n_atom_list, level)
def search_all(self, radius, level="A"):
"""All neighbor search.
Search all entities that have atoms pairs within
radius.
Arguments:
- radius - float
- level - char (A, R, C, M, S)
"""
if level not in entity_levels:
raise PDBException("%s: Unknown level" % level)
self.kdt.all_search(radius)
indices = self.kdt.all_get_indices()
atom_list = self.atom_list
atom_pair_list = []
for i1, i2 in indices:
a1 = atom_list[i1]
a2 = atom_list[i2]
atom_pair_list.append((a1, a2))
if level == "A":
# return atoms
return atom_pair_list
next_level_pair_list = atom_pair_list
for l in ["R", "C", "M", "S"]:
next_level_pair_list = self._get_unique_parent_pairs(next_level_pair_list)
if level == l:
return next_level_pair_list
class NeighborSearch(object):
"""Class for neighbor searching,
This class can be used for two related purposes:
1. To find all atoms/residues/chains/models/structures within radius
of a given query position.
2. To find all atoms/residues/chains/models/structures that are within
a fixed radius of each other.
NeighborSearch makes use of the Bio.KDTree C++ module, so it's fast.
"""
def __init__(self, atom_list, bucket_size=10):
"""Create the object.
Arguments:
- atom_list - list of atoms. This list is used in the queries.
It can contain atoms from different structures.
- bucket_size - bucket size of KD tree. You can play around
with this to optimize speed if you feel like it.
"""
self.atom_list = atom_list
# get the coordinates
coord_list = [a.get_coord() for a in atom_list]
# to Nx3 array of type float
self.coords = numpy.array(coord_list).astype("f")
assert (bucket_size > 1)
assert (self.coords.shape[1] == 3)
self.kdt = KDTree(3, bucket_size)
self.kdt.set_coords(self.coords)
# Private
def _get_unique_parent_pairs(self, pair_list):
# translate a list of (entity, entity) tuples to
# a list of (parent entity, parent entity) tuples,
# thereby removing duplicate (parent entity, parent entity)
# pairs.
# o pair_list - a list of (entity, entity) tuples
parent_pair_list = []
for (e1, e2) in pair_list:
p1 = e1.get_parent()
p2 = e2.get_parent()
if p1 == p2:
continue
elif p1 < p2:
parent_pair_list.append((p1, p2))
else:
parent_pair_list.append((p2, p1))
return uniqueify(parent_pair_list)
# Public
def search(self, center, radius, level="A"):
"""Neighbor search.
Return all atoms/residues/chains/models/structures
that have at least one atom within radius of center.
What entity level is returned (e.g. atoms or residues)
is determined by level (A=atoms, R=residues, C=chains,
M=models, S=structures).
Arguments:
- center - Numeric array
- radius - float
- level - char (A, R, C, M, S)
"""
if level not in entity_levels:
raise PDBException("%s: Unknown level" % level)
self.kdt.search(center, radius)
indices = self.kdt.get_indices()
n_atom_list = []
atom_list = self.atom_list
for i in indices:
a = atom_list[i]
n_atom_list.append(a)
if level == "A":
return n_atom_list
else:
return unfold_entities(n_atom_list, level)
def search_all(self, radius, level="A"):
"""All neighbor search.
Search all entities that have atoms pairs within
radius.
Arguments:
- radius - float
- level - char (A, R, C, M, S)
"""
if level not in entity_levels:
raise PDBException("%s: Unknown level" % level)
self.kdt.all_search(radius)
indices = self.kdt.all_get_indices()
atom_list = self.atom_list
atom_pair_list = []
for i1, i2 in indices:
a1 = atom_list[i1]
a2 = atom_list[i2]
atom_pair_list.append((a1, a2))
if level == "A":
# return atoms
return atom_pair_list
next_level_pair_list = atom_pair_list
for l in ["R", "C", "M", "S"]:
next_level_pair_list = self._get_unique_parent_pairs(
next_level_pair_list)
if level == l:
return next_level_pair_list
