def _apply_distmat(self, group):
"""Selection that computes all distances."""
sys_indices = numpy.array([a.number for a in self._group_atoms_list])
sys_coor = Selection.coord[sys_indices]
ref_coor = self.ref[numpy.newaxis, ...]
# Fix: Arrarys need to be converted to dtype=float32 to work with distance_array
sys_coor = numpy.asarray(sys_coor, dtype=numpy.float32)
ref_coor = numpy.asarray(ref_coor, dtype=numpy.float32)
if self.periodic:
box = group.dimensions[:3]
else:
box = None
import distances
dist = distances.distance_array(sys_coor, ref_coor, box)
res_atoms = [self._group_atoms_list[i] for i in numpy.any(dist <= self.cutoff, axis=1).nonzero()[0]]
# make list numpy array and use fancy indexing?
return set(res_atoms)
def _apply_distmat(self, group):
sel_atoms = self.sel._apply(group) # group is wrong, should be universe (?!)
sys_atoms_list = [a for a in (self._group_atoms - sel_atoms)] # list needed for back-indexing
sel_indices = numpy.array([a.number for a in sel_atoms], dtype=int)
sys_indices = numpy.array([a.number for a in sys_atoms_list], dtype=int)
sel_coor = Selection.coord[sel_indices]
sys_coor = Selection.coord[sys_indices]
if self.periodic:
box = group.dimensions[:3] # ignored with KDTree
else:
box = None
import distances
dist = distances.distance_array(sys_coor, sel_coor, box)
res_atoms = [
sys_atoms_list[i] for i in
numpy.any(dist <= self.cutoff, axis=1).nonzero()[0]] # make list numpy array and use fancy indexing?
return set(res_atoms)
def _apply_distmat(self, group):
"""Selection that computes all distances."""
sys_indices = numpy.array([a.number for a in self._group_atoms_list])
sys_coor = Selection.coord[sys_indices]
ref_coor = self.ref[numpy.newaxis, ...]
# Fix: Arrarys need to be converted to dtype=float32 to work with distance_array
sys_coor = numpy.asarray(sys_coor, dtype=numpy.float32)
ref_coor = numpy.asarray(ref_coor, dtype=numpy.float32)
if self.periodic:
box = group.dimensions[:3]
else:
box = None
import distances
dist = distances.distance_array(sys_coor, ref_coor, box)
res_atoms = [
self._group_atoms_list[i]
for i in numpy.any(dist <= self.cutoff, axis=1).nonzero()[0]
]
# make list numpy array and use fancy indexing?
return set(res_atoms)
def _apply_distmat(self, group):
sel_atoms = self.sel._apply(
group) # group is wrong, should be universe (?!)
sys_atoms_list = [a for a in (self._group_atoms - sel_atoms)
] # list needed for back-indexing
sel_indices = numpy.array([a.number for a in sel_atoms], dtype=int)
sys_indices = numpy.array([a.number for a in sys_atoms_list],
dtype=int)
sel_coor = Selection.coord[sel_indices]
sys_coor = Selection.coord[sys_indices]
if self.periodic:
box = group.dimensions[:3] # ignored with KDTree
else:
box = None
import distances
dist = distances.distance_array(sys_coor, sel_coor, box)
res_atoms = [
sys_atoms_list[i]
for i in numpy.any(dist <= self.cutoff, axis=1).nonzero()[0]
] # make list numpy array and use fancy indexing?
return set(res_atoms)
