class CoordinateNeighborSearch(object):
"""
This class can be used for two related purposes:
1. To find all indices of a coordinate list within radius
of a given query position.
2. To find all indices of a coordinate list that are within
a fixed radius of each other.
CoordinateNeighborSearch makes use of the KDTree C++ module, so it's fast.
"""
def __init__(self, coordinates, bucket_size=10): # , copy=True):
"""
:Arguments:
*coordinates*
list of N coordinates (Nx3 numpy array)
*bucket_size*
bucket size of KD tree. You can play around with this to
optimize speed if you feel like it.
"""
# to Nx3 array of type float (required for the C++ code)
## (also force a copy by default and make sure that the array order is compatible
## with the C++ code)
##self.coords=numpy.array(coordinates,dtype=numpy.float32,copy=copy,order='C')
self.coords = numpy.asarray(coordinates, dtype=numpy.float32, order='C')
assert (self.coords.dtype == numpy.float32)
assert (bucket_size > 1)
assert (self.coords.shape[1] == 3)
self.kdt = KDTree(3, bucket_size)
self.kdt.set_coords(self.coords)
def search(self, center, radius, distances=False):
"""Neighbor search.
Return all indices in the coordinates list that have at least
one atom within *radius* of *center*.
:Arguments:
* center
numpy array
* radius
float
* distances
bool ``True``: return (indices,distances); ``False``: return indices only
"""
self.kdt.search(center, radius)
if distances:
return self.kdt.get_indices(), self.kdt.get_radii()
else:
return self.kdt.get_indices()
def search_list(self, centers, radius):
"""Search neighbours near all centers.
Returns all indices that are within *radius* of any center listed in
*centers*, i.e. "find all A within R of B" where A are the
coordinates used for setting up the CoordinateNeighborSearch and B
are the centers.
:Arguments:
*centers*
Mx3 numpy array of M centers
*radius*
float
"""
self.kdt.list_search(centers, radius)
return self.kdt.list_get_indices()
def search_all(self, radius, distances=False):
"""All neighbor search.
Return all index pairs corresponding to coordinates within the *radius*.
:Arguments:
*radius*
float
*distances*
bool ``True``: return (indices,distances); ``False``: return indices only
[``False``]
"""
self.kdt.all_search(radius)
if distances:
return self.kdt.all_get_indices(), self.kdt.all_get_radii()
else:
return self.kdt.all_get_indices()
def _distances(self):
"""Return all distances after search()."""
return self.kdt.get_radii()
def _distances_all(self):
"""Return all distances after search_all()."""
return self.kdt.all_get_radii()
class CoordinateNeighborSearch(object):
"""
This class can be used for two related purposes:
1. To find all indices of a coordinate list within radius
of a given query position.
2. To find all indices of a coordinate list that are within
a fixed radius of each other.
CoordinateNeighborSearch makes use of the KDTree C++ module, so it's fast.
"""
def __init__(self, coordinates, bucket_size=10): # , copy=True):
"""
:Arguments:
*coordinates*
list of N coordinates (Nx3 numpy array)
*bucket_size*
bucket size of KD tree. You can play around with this to
optimize speed if you feel like it.
"""
# to Nx3 array of type float (required for the C++ code)
## (also force a copy by default and make sure that the array order is compatible
## with the C++ code)
##self.coords=numpy.array(coordinates,dtype=numpy.float32,copy=copy,order='C')
self.coords = numpy.asarray(coordinates,
dtype=numpy.float32,
order='C')
assert (self.coords.dtype == numpy.float32)
assert (bucket_size > 1)
assert (self.coords.shape[1] == 3)
self.kdt = KDTree(3, bucket_size)
self.kdt.set_coords(self.coords)
def search(self, center, radius, distances=False):
"""Neighbor search.
Return all indices in the coordinates list that have at least
one atom within *radius* of *center*.
:Arguments:
* center
numpy array
* radius
float
* distances
bool ``True``: return (indices,distances); ``False``: return indices only
"""
self.kdt.search(center, radius)
if distances:
return self.kdt.get_indices(), self.kdt.get_radii()
else:
return self.kdt.get_indices()
def search_list(self, centers, radius):
"""Search neighbours near all centers.
Returns all indices that are within *radius* of any center listed in
*centers*, i.e. "find all A within R of B" where A are the
coordinates used for setting up the CoordinateNeighborSearch and B
are the centers.
:Arguments:
*centers*
Mx3 numpy array of M centers
*radius*
float
"""
self.kdt.list_search(centers, radius)
return self.kdt.list_get_indices()
def search_all(self, radius, distances=False):
"""All neighbor search.
Return all index pairs corresponding to coordinates within the *radius*.
:Arguments:
*radius*
float
*distances*
bool ``True``: return (indices,distances); ``False``: return indices only
[``False``]
"""
self.kdt.all_search(radius)
if distances:
return self.kdt.all_get_indices(), self.kdt.all_get_radii()
else:
return self.kdt.all_get_indices()
def _distances(self):
"""Return all distances after search()."""
return self.kdt.get_radii()
def _distances_all(self):
"""Return all distances after search_all()."""
return self.kdt.all_get_radii()
