def _cart_dists(self, s1, s2, avg_lattice, mask, normalization, frac_tol=None):
"""
Finds a matching in cartesian space. Finds an additional
fractional translation vector to minimize RMS distance
Args:
s1, s2: numpy arrays of fractional coordinates. len(s1) >= len(s2)
avg_lattice: Lattice on which to calculate distances
mask: numpy array of booleans. mask[i, j] = True indicates
that s2[i] cannot be matched to s1[j]
normalization (float): inverse normalization length
Returns:
Distances from s2 to s1, normalized by (V/Natom) ^ 1/3
Fractional translation vector to apply to s2.
Mapping from s1 to s2, i.e. with numpy slicing, s1[mapping] => s2
"""
if len(s2) > len(s1):
raise ValueError("s1 must be larger than s2")
if mask.shape != (len(s2), len(s1)):
raise ValueError("mask has incorrect shape")
#vectors are from s2 to s1
vecs, d_2 = pbc_shortest_vectors(avg_lattice, s2, s1, mask, return_d2=True, frac_tol=frac_tol)
lin = LinearAssignment(d_2)
s = lin.solution
short_vecs = vecs[np.arange(len(s)), s]
translation = np.average(short_vecs, axis=0)
f_translation = avg_lattice.get_fractional_coords(translation)
new_d2 = np.sum((short_vecs - translation) ** 2, axis=-1)
return new_d2 ** 0.5 * normalization, f_translation, s
def _cart_dists(self, s1, s2, avg_lattice, mask, normalization, frac_tol=None):
"""
Finds a matching in cartesian space. Finds an additional
fractional translation vector to minimize RMS distance
Args:
s1, s2: numpy arrays of fractional coordinates. len(s1) >= len(s2)
avg_lattice: Lattice on which to calculate distances
mask: numpy array of booleans. mask[i, j] = True indicates
that s2[i] cannot be matched to s1[j]
normalization (float): inverse normalization length
Returns:
Distances from s2 to s1, normalized by (V/Natom) ^ 1/3
Fractional translation vector to apply to s2.
Mapping from s1 to s2, i.e. with numpy slicing, s1[mapping] => s2
"""
if len(s2) > len(s1):
raise ValueError("s1 must be larger than s2")
if mask.shape != (len(s2), len(s1)):
raise ValueError("mask has incorrect shape")
#vectors are from s2 to s1
vecs, d_2 = pbc_shortest_vectors(avg_lattice, s2, s1, mask, return_d2=True, frac_tol=frac_tol)
lin = LinearAssignment(d_2)
s = lin.solution
short_vecs = vecs[np.arange(len(s)), s]
translation = np.average(short_vecs, axis=0)
f_translation = avg_lattice.get_fractional_coords(translation)
new_d2 = np.sum((short_vecs - translation) ** 2, axis=-1)
return new_d2 ** 0.5 * normalization, f_translation, s
def pbc_shortest_vectors(lattice, fcoords1, fcoords2, mask=None, return_d2=False):
"""
Returns the shortest vectors between two lists of coordinates taking into
account periodic boundary conditions and the lattice.
Args:
lattice: lattice to use
fcoords1: First set of fractional coordinates. e.g., [0.5, 0.6, 0.7]
or [[1.1, 1.2, 4.3], [0.5, 0.6, 0.7]]. It can be a single
coord or any array of coords.
fcoords2: Second set of fractional coordinates.
mask (boolean array): Mask of matches that are not allowed.
i.e. if mask[1,2] == True, then subset[1] cannot be matched
to superset[2]
return_d2 (boolean): whether to also return the squared distances
Returns:
array of displacement vectors from fcoords1 to fcoords2
first index is fcoords1 index, second is fcoords2 index
"""
return cuc.pbc_shortest_vectors(lattice, fcoords1, fcoords2, mask, return_d2)
def pbc_shortest_vectors(lattice, fcoords1, fcoords2, mask=None, return_d2=False):
"""
Returns the shortest vectors between two lists of coordinates taking into
account periodic boundary conditions and the lattice.
Args:
lattice: lattice to use
fcoords1: First set of fractional coordinates. e.g., [0.5, 0.6, 0.7]
or [[1.1, 1.2, 4.3], [0.5, 0.6, 0.7]]. It can be a single
coord or any array of coords.
fcoords2: Second set of fractional coordinates.
mask (boolean array): Mask of matches that are not allowed.
i.e. if mask[1,2] == True, then subset[1] cannot be matched
to superset[2]
return_d2 (boolean): whether to also return the squared distances
Returns:
array of displacement vectors from fcoords1 to fcoords2
first index is fcoords1 index, second is fcoords2 index
"""
return cuc.pbc_shortest_vectors(lattice, fcoords1, fcoords2, mask, return_d2)
