def _atoms_too_close(self, iatom):
"""Check if any two atoms are too close, i.e. closer then the sum of
their covalent radii, scaled by self.close_scale.
Minimum image distances are used.
Parameters
----------
iatom : int
Index into self.coords_frac, defining the number of atoms currently
in there (iatom +1).
"""
# dist: min image dist correct only up to rmax_smith(), but we check if
# atoms are too close; too big dists are no problem; choose only upper
# triangle from array `dist`
natoms_filled = iatom + 1
coords_frac = self.coords_frac[:natoms_filled,:]
# This part is 10x slower than the fortran version --------
# distvecs_frac: (natoms_filled, natoms_filled, 3)
# distvecs: (natoms_filled, natoms_filled, 3)
# dist: (natoms_filled, natoms_filled)
##distvecs_frac = coords_frac[:,None,:] - coords_frac[None,:,:]
##distvecs_frac = min_image_convention(sij)
##distvecs = np.dot(sij, self.cell)
##dist = np.sqrt((rij**2.0).sum(axis=2))
#-----------------------------------------------------------
nn = natoms_filled
# XXX For maximum speed: dummy1 and dummy2 may be big and are allocated
# each time this method is called, which may be *many* times. Either
# re-write the fortran code to not require them as input or allocate
# them in __init__().
distsq,dummy1,dummy2 = fempty((nn,nn)), fempty((nn,nn,3)), \
fempty((nn,nn,3))
distsq, dummy1, dummy2 = _flib.distsq_frac(coords_frac,
self.cell,
1,
distsq,
dummy1,
dummy2)
dist = np.sqrt(distsq)
# This part is fast
dij_min_filled = self.dij_min[:natoms_filled,:natoms_filled]
return np.triu(dist < dij_min_filled, k=1).any()
def _atoms_too_close(self, iatom):
"""Check if any two atoms are too close, i.e. closer then the sum of
their covalent radii, scaled by self.close_scale.
Minimum image distances are used.
Parameters
----------
iatom : int
Index into self.coords_frac, defining the number of atoms currently
in there (iatom +1).
"""
# dist: min image dist correct only up to rmax_smith(), but we check if
# atoms are too close; too big dists are no problem; choose only upper
# triangle from array `dist`
natoms_filled = iatom + 1
coords_frac = self.coords_frac[:natoms_filled, :]
# This part is 10x slower than the fortran version --------
# distvecs_frac: (natoms_filled, natoms_filled, 3)
# distvecs: (natoms_filled, natoms_filled, 3)
# dist: (natoms_filled, natoms_filled)
##distvecs_frac = coords_frac[:,None,:] - coords_frac[None,:,:]
##distvecs_frac = min_image_convention(sij)
##distvecs = np.dot(sij, self.cell)
##dist = np.sqrt((rij**2.0).sum(axis=2))
#-----------------------------------------------------------
nn = natoms_filled
# XXX For maximum speed: dummy1 and dummy2 may be big and are allocated
# each time this method is called, which may be *many* times. Either
# re-write the fortran code to not require them as input or allocate
# them in __init__().
distsq,dummy1,dummy2 = fempty((nn,nn)), fempty((nn,nn,3)), \
fempty((nn,nn,3))
distsq, dummy1, dummy2 = _flib.distsq_frac(coords_frac, self.cell, 1,
distsq, dummy1, dummy2)
dist = np.sqrt(distsq)
# This part is fast
dij_min_filled = self.dij_min[:natoms_filled, :natoms_filled]
return np.triu(dist < dij_min_filled, k=1).any()
def fdist(arr, cell, pbc=0):
natoms = arr.shape[0]
distsq = num.fempty((natoms,natoms))
dummy1 = num.fempty((natoms,natoms,3))
dummy2 = num.fempty((natoms,natoms,3))
return _flib.distsq_frac(arr, cell, pbc, distsq, dummy1, dummy2)
def fdist(arr, cell, pbc=0):
natoms = arr.shape[0]
distsq = num.fempty((natoms, natoms))
dummy1 = num.fempty((natoms, natoms, 3))
dummy2 = num.fempty((natoms, natoms, 3))
return _flib.distsq_frac(arr, cell, pbc, distsq, dummy1, dummy2)
