class KDTree(object):
"""An interface to Thomas Hamelryck's C KDTree module that can handle
periodic boundary conditions. Both point and pair search are performed
using the single :meth:`search` method and results are retrieved using
:meth:`getIndices` and :meth:`getDistances`.
**Periodic Boundary Conditions**
*Point search*
A point search around a *center*, indicated with a question mark (``?``)
below, involves making images of the point in cells sharing a wall or an
edge with the unitcell that contains the system. The search is performed
for all images of the *center* (27 in 3-dimensional space) and unique
indices with the minimum distance from them to the *center* are returned.
::
_____________________________
| 1| 2| 3|
| ? | ? | ? |
|_________|_________|_________|
| 4|o h h 5| 6| ? and H interact in periodic image 4
| ?H| h o ? | ? | but not in the original unitcell (0)
|_________|_________|_________|
| 7| 8| 9|
| ? | ? | ? |
|_________|_________|_________|
There are two requirements for this approach to work: (i) the *center* must
be in the original unitcell, and (ii) the system must be in the original
unitcell with parts in its immediate periodic images.
*Pair search*
A pair search involves making 26 (or 8 in 2-d) replicas of the system
coordinates. A KDTree is built for the system (``O`` and ``H``) and all
its replicas (``o`` and ``h``). After pair search is performed, unique
pairs of indices and minimum distance between them are returned.
::
_____________________________
|o h h 1|o h h 2|o h h 3|
h| h o h| h o h| h o |
|_________|_________|_________|
|o h h 4|O H H 5|o h h 6|
h| h o H| H O h| h o |
|_________|_________|_________|
|o h h 7|o h h 8|o h h 9|
h| h o h| h o h| h o |
|_________|_________|_________|
Only requirement for this approach to work is that the system must be
in the original unitcell with parts in its immediate periodic images.
"""
def __init__(self, coords, **kwargs):
"""
:arg coords: coordinate array with shape ``(N, 3)``, where N is number
of atoms
:type coords: :class:`numpy.ndarray`, :class:`.Atomic`, :class:`.Frame`
:arg unitcell: orthorhombic unitcell dimension array with shape
``(3,)``
:type unitcell: :class:`numpy.ndarray`
:arg bucketsize: number of points per tree node, default is 10
:type bucketsize: int"""
unitcell = kwargs.get('unitcell')
if not isinstance(coords, ndarray):
if unitcell is None:
try:
unitcell = coords.getUnitcell()
except AttributeError:
pass
else:
if unitcell is not None:
LOGGER.info('Unitcell information from {0:s} will be '
'used.'.format(str(coords)))
try:
# using getCoords() because coords will be stored internally
# and reused when needed, this will avoid unexpected results
# due to changes made to coordinates externally
coords = coords.getCoords()
except AttributeError:
raise TypeError('coords must be a Numpy array or must have '
'getCoords attribute')
else:
coords = coords.copy()
if coords.ndim != 2:
raise Exception('coords.ndim must be 2')
if coords.shape[-1] != 3:
raise Exception('coords.shape must be (N,3)')
if coords.min() <= -1e6 or coords.max() >= 1e6:
raise Exception('coords must be between -1e6 and 1e6')
self._bucketsize = kwargs.get('bucketsize', 10)
if not isinstance(self._bucketsize, int):
raise TypeError('bucketsize must be an integer')
if self._bucketsize < 1:
raise ValueError('bucketsize must be a positive integer')
self._coords = None
self._unitcell = None
self._neighbors = None
if unitcell is None:
self._kdtree = CKDTree(3, self._bucketsize)
self._kdtree.set_data(coords)
else:
if not isinstance(unitcell, ndarray):
raise TypeError('unitcell must be a Numpy array')
if unitcell.shape != (3,):
raise ValueError('unitcell.shape must be (3,)')
self._kdtree = CKDTree(3, self._bucketsize)
self._kdtree.set_data(coords)
self._coords = coords
self._unitcell = unitcell
self._replicate = REPLICATE * unitcell
self._kdtree2 = None
self._pbcdict = {}
self._pbckeys = []
self._n_atoms = coords.shape[0]
self._none = kwargs.pop('none', lambda: None)
assert callable(self._none), 'none argument must be callable'
self._oncall = kwargs.pop('oncall', 'both')
assert self._oncall in ('both', 'dist'), 'oncall must be both or dist'
def __call__(self, radius, center=None):
"""Shorthand method for searching and retrieving results."""
self.search(radius, center)
if self._oncall == 'both':
return self.getIndices(), self.getDistances()
elif self._oncall == 'dist':
return self.getDistances()
def search(self, radius, center=None):
"""Search pairs within *radius* of each other or points within *radius*
of *center*.
:arg radius: distance (Å)
:type radius: float
:arg center: a point in Cartesian coordinate system
:type center: :class:`numpy.ndarray`"""
if not isinstance(radius, (float, int)):
raise TypeError('radius must be a number')
if radius <= 0:
raise TypeError('radius must be a positive number')
if center is not None:
if not isinstance(center, ndarray):
raise TypeError('center must be a Numpy array instance')
if center.shape != (3,):
raise ValueError('center.shape must be (3,)')
if self._unitcell is None:
self._kdtree.search_center_radius(center, radius)
self._neighbors = None
else:
kdtree = self._kdtree
search = kdtree.search_center_radius
get_radii = kdtree.get_radii
get_indices = kdtree.get_indices
get_count = kdtree.get_count
_dict = {}
_dict_get = _dict.get
_dict_set = _dict.__setitem__
for center in center + self._replicate:
search(center, radius)
if get_count():
[_dict_set(i, min(r, _dict_get(i, 1e6)))
for i, r in zip(get_indices(), get_radii())]
self._pbcdict = _dict
self._pdbkeys = _dict.keys()
else:
if self._unitcell is None:
self._neighbors = self._kdtree.neighbor_search(radius)
else:
kdtree = self._kdtree2
if kdtree is None:
coords = self._coords
coords = concatenate([coords + rep
for rep in self._replicate])
kdtree = CKDTree(3, self._bucketsize)
kdtree.set_data(coords)
self._kdtree2 = kdtree
n_atoms = len(self._coords)
_dict = {}
neighbors = kdtree.neighbor_search(radius)
if kdtree.neighbor_get_count():
_get = _dict.get
_set = _dict.__setitem__
for nb in neighbors:
i = nb.index1 % n_atoms
j = nb.index2 % n_atoms
if i < j:
_set((i, j), min(nb.radius, _get((i, j), 1e6)))
elif j < i:
_set((j, i), min(nb.radius, _get((j, i), 1e6)))
self._pbcdict = _dict
self._pdbkeys = _dict.keys()
def getIndices(self):
"""Return array of indices for points or pairs, depending on the type
of the most recent search."""
if self.getCount():
if self._unitcell is None:
if self._neighbors is None:
return self._kdtree.get_indices()
else:
return array([(n.index1, n.index2)
for n in self._neighbors], int)
else:
return array(self._pdbkeys)
return self._none()
def getDistances(self):
"""Return array of distances."""
if self.getCount():
if self._unitcell is None:
if self._neighbors is None:
return self._kdtree.get_radii()
else:
return array([n.radius for n in self._neighbors])
else:
_dict = self._pbcdict
return array([_dict[i] for i in self._pdbkeys])
return self._none()
def getCount(self):
"""Return number of points or pairs."""
if self._unitcell is None:
if self._neighbors is None:
return self._kdtree.get_count()
else:
return self._kdtree.neighbor_get_count()
else:
return len(self._pbcdict)
def __init__(self, coords, **kwargs):
"""
:arg coords: coordinate array with shape ``(N, 3)``, where N is number
of atoms
:type coords: :class:`numpy.ndarray`, :class:`.Atomic`, :class:`.Frame`
:arg unitcell: orthorhombic unitcell dimension array with shape
``(3,)``
:type unitcell: :class:`numpy.ndarray`
:arg bucketsize: number of points per tree node, default is 10
:type bucketsize: int"""
unitcell = kwargs.get('unitcell')
if not isinstance(coords, ndarray):
if unitcell is None:
try:
unitcell = coords.getUnitcell()
except AttributeError:
pass
else:
if unitcell is not None:
LOGGER.info('Unitcell information from {0:s} will be '
'used.'.format(str(coords)))
try:
# using getCoords() because coords will be stored internally
# and reused when needed, this will avoid unexpected results
# due to changes made to coordinates externally
coords = coords.getCoords()
except AttributeError:
raise TypeError('coords must be a Numpy array or must have '
'getCoords attribute')
else:
coords = coords.copy()
if coords.ndim != 2:
raise Exception('coords.ndim must be 2')
if coords.shape[-1] != 3:
raise Exception('coords.shape must be (N,3)')
if coords.min() <= -1e6 or coords.max() >= 1e6:
raise Exception('coords must be between -1e6 and 1e6')
self._bucketsize = kwargs.get('bucketsize', 10)
if not isinstance(self._bucketsize, int):
raise TypeError('bucketsize must be an integer')
if self._bucketsize < 1:
raise ValueError('bucketsize must be a positive integer')
self._coords = None
self._unitcell = None
self._neighbors = None
if unitcell is None:
self._kdtree = CKDTree(3, self._bucketsize)
self._kdtree.set_data(coords)
else:
if not isinstance(unitcell, ndarray):
raise TypeError('unitcell must be a Numpy array')
if unitcell.shape != (3,):
raise ValueError('unitcell.shape must be (3,)')
self._kdtree = CKDTree(3, self._bucketsize)
self._kdtree.set_data(coords)
self._coords = coords
self._unitcell = unitcell
self._replicate = REPLICATE * unitcell
self._kdtree2 = None
self._pbcdict = {}
self._pbckeys = []
self._n_atoms = coords.shape[0]
self._none = kwargs.pop('none', lambda: None)
assert callable(self._none), 'none argument must be callable'
self._oncall = kwargs.pop('oncall', 'both')
assert self._oncall in ('both', 'dist'), 'oncall must be both or dist'
def search(self, radius, center=None):
"""Search pairs within *radius* of each other or points within *radius*
of *center*.
:arg radius: distance (Å)
:type radius: float
:arg center: a point in Cartesian coordinate system
:type center: :class:`numpy.ndarray`"""
if not isinstance(radius, (float, int)):
raise TypeError('radius must be a number')
if radius <= 0:
raise TypeError('radius must be a positive number')
if center is not None:
if not isinstance(center, ndarray):
raise TypeError('center must be a Numpy array instance')
if center.shape != (3,):
raise ValueError('center.shape must be (3,)')
if self._unitcell is None:
self._kdtree.search_center_radius(center, radius)
self._neighbors = None
else:
kdtree = self._kdtree
search = kdtree.search_center_radius
get_radii = kdtree.get_radii
get_indices = kdtree.get_indices
get_count = kdtree.get_count
_dict = {}
_dict_get = _dict.get
_dict_set = _dict.__setitem__
for center in center + self._replicate:
search(center, radius)
if get_count():
[_dict_set(i, min(r, _dict_get(i, 1e6)))
for i, r in zip(get_indices(), get_radii())]
self._pbcdict = _dict
self._pdbkeys = _dict.keys()
else:
if self._unitcell is None:
self._neighbors = self._kdtree.neighbor_search(radius)
else:
kdtree = self._kdtree2
if kdtree is None:
coords = self._coords
coords = concatenate([coords + rep
for rep in self._replicate])
kdtree = CKDTree(3, self._bucketsize)
kdtree.set_data(coords)
self._kdtree2 = kdtree
n_atoms = len(self._coords)
_dict = {}
neighbors = kdtree.neighbor_search(radius)
if kdtree.neighbor_get_count():
_get = _dict.get
_set = _dict.__setitem__
for nb in neighbors:
i = nb.index1 % n_atoms
j = nb.index2 % n_atoms
if i < j:
_set((i, j), min(nb.radius, _get((i, j), 1e6)))
elif j < i:
_set((j, i), min(nb.radius, _get((j, i), 1e6)))
self._pbcdict = _dict
self._pdbkeys = _dict.keys()