def __str__(self):
ret_val = "CartesianRepresentation of molecule {}, in {}:\n".format(
shortstr(self.molecule), self.units)
for i, (x, y, z) in enumerate(grouper(3, self)):
ret_val += indent(
'{:>9} {:12.8f} {:12.8f} {:12.8f}\n'.format(
'[{}-{}]'.format(3 * i, 3 * i + 2), x.value, y.value,
z.value), 2)
return ret_val
def __str__(self):
ret_val = "CartesianRepresentation of molecule {}, in {}:\n".format(shortstr(self.molecule), self.units)
for i, (x, y, z) in enumerate(grouper(3, self)):
ret_val += indent(
"{:>9} {:12.8f} {:12.8f} {:12.8f}\n".format(
"[{}-{}]".format(3 * i, 3 * i + 2), x.value, y.value, z.value
),
2,
)
return ret_val
def task_description(self):
# TODO add some more helpful information to this, with an argument for verbosity level
ret_val = ""
basics = [
"Path", self.directory,
"Input File Name", self.runner.input_file.split(os.sep)[-1],
"Output File Name", self.runner.output_file.split(os.sep)[-1],
]
extras = [
"Input Generator Class", self.input_generator.__class__.__name__,
"Output Parser Class", self.output_parser.__class__.__name__,
"Runner Class", self.runner.__class__.__name__,
]
allstats = basics + extras
lwidth = max(len(k) for k, v in grouper(2, allstats))
rwidth = max(len(v) for k, v in grouper(2, allstats))
props = []
for name, value in grouper(2, allstats):
props.append(('{:.<'+str(lwidth)+'}{:.>'+str(rwidth)+'}').format(
name, value
))
return '\n'.join(props)
def coordinate_with_cartesian_displacements(self, base_coord, disps):
disps = tuple(disps)
if all(d == 0 for d in disps):
return base_coord
ret_val = self.displaced_coordinates.get((base_coord, disps), None)
if ret_val is None:
new_atoms = []
for atom, disp in zip(base_coord.atoms, grouper(3, disps)):
new_atoms.append(atom.displaced(LightVector(disp)*self.delta))
ret_val = base_coord.copy_with_atoms(new_atoms)
# Cache it for later to avoid gratuitous creation of displaced Molecule instances
self.displaced_coordinates[(base_coord, disps)] = ret_val
return ret_val
def value_for_displacements(self, pairs):
# get the position vector
xyzvect = LightVector([a.position for a in self.atoms]).ravel()
# make pairs into a list in case it's a general iterator
pairs = list(pairs)
int_idxs = self.internal_indices_for_coordinates(*[pair[0] for pair in pairs])
for i, (__, ndeltas) in enumerate(pairs):
xyzvect[int_idxs[i]] += self.b_tensor_finite_difference_delta * ndeltas
return self.__class__.value_for_xyz(
Matrix(
list(grouper(3, xyzvect))
)
)
def iter_atom_coords(self, with_atom=False):
for i, (a, b, c) in enumerate(grouper(3, self)):
if with_atom:
yield a, b, c, self.atoms[i]
else:
yield a, b, c
def transform_tensor(self, tensor, to_representation):
"""
"""
self.freeze()
to_representation.freeze()
shape = (len(to_representation), ) * len(tensor.shape)
ret_val = RepresentationDependentTensor(
shape=shape, representation=to_representation)
#--------------------------------------------------------------------------------#
if isinstance(to_representation, CartesianRepresentation):
if len(self) != len(to_representation):
raise ValueError(
"incompatible representation sizes ({} != {})".format(
len(self), len(to_representation)))
if tensor.representation is not self:
raise ValueError(
"representation {} can only transform a tensor whose representation attribute is the same "
" as itself (tensor.representation was {} ".format(
self, tensor.representation))
if self.molecule.is_linear():
raise NotImplementedError(
"linear molecule cartesian-to-cartesian transformation is not"
" yet implemented. Shouldn't be too hard...")
if sanity_checking_enabled:
#TODO use a recentered version of the 'from' representation if it is not centered
pass
# Make sure things are centered
#if not self.molecule.is_centered(cartesian_representation=self):
# raise ValueError("CartesianRepresentation objects transformed from and to"
# " must be centered at the center of mass ({} was not)".format(
# self
# ))
#elif not self.molecule.is_centered(cartesian_representation=to_representation):
# raise ValueError("CartesianRepresentation objects transformed from and to"
# " must be centered at the center of mass ({} was not)".format(
# to_representation
# ))
#----------------------------------------#
old = self.value
new = to_representation.value
unitconv = self.units.to(to_representation.units)
oldmat = Matrix(old).reshape((len(self) / 3, 3))
newmat = Matrix(new).reshape((len(self) / 3, 3))
#----------------------------------------#
# Check to see if the molecule is planar. If so, append the cross product of any
# two atoms' positions not colinear with the origin
if any(norm(oldmat[:dir].view(Vector)) < 1e-12 \
or norm(newmat[:dir].view(Vector)) < 1e-12 \
for dir in [X, Y, Z]):
first_atom = None
# TODO unit concious cutoff (e.g. this would fail if the units of the position matrix were meters)
nonorigin_cutoff = 1e-2
for i, v in enumerate(grouper(3, old)):
if norm(LightVector(v)) > nonorigin_cutoff:
first_atom = i
break
second_atom = None
for i in range(first_atom + 1, len(self) // 3):
#TODO make sure this works with Molecule.linear_cutoff
if norm(oldmat[i]) > nonorigin_cutoff and norm(newmat[i]) > nonorigin_cutoff \
and abs(angle_between_vectors(oldmat[first_atom], oldmat[i])) > 1e-5 \
and abs(angle_between_vectors(newmat[first_atom], newmat[i])) > 1e-5:
second_atom = i
break
oldmat = Matrix(
list(oldmat.rows) +
[cross(oldmat[first_atom], oldmat[second_atom])])
newmat = Matrix(
list(newmat.rows) +
[cross(newmat[first_atom], newmat[second_atom])])
#----------------------------------------#
rot_mat = newmat.T * np.linalg.pinv(oldmat.T)
# Divide by unit conversion because the representation dependent tensor
# is [some units] *per* representation units
rot_mat /= unitconv
trans_mat = Matrix(shape=(len(self), len(self)))
for idx in xrange(len(self) // 3):
off = idx * 3
trans_mat[off:off + 3, off:off + 3] = rot_mat
order = len(tensor.shape)
# This is basically impossible to read what I'm doing here, but work it out
# looking at the numpy.einsum documentation and you'll see that this is correct.
# Basically, we want to contract the row indices of the transformation matrix
# with each axis of the tensor to be transformed.
einsumargs = sum(
([trans_mat, [2 * i, 2 * i + 1]] for i in xrange(order)), [])
einsumargs += [
tensor, [i for i in xrange(2 * order) if i % 2 != 0]
]
einsumargs += [[i for i in xrange(2 * order) if i % 2 == 0]]
np.einsum(*einsumargs, out=ret_val)
return ret_val
#--------------------------------------------------------------------------------#
elif isinstance(to_representation, InternalRepresentation):
if len(tensor.shape) == 1:
A = to_representation.a_matrix
ret_val[...] = A.T * tensor.view(Vector)
else:
raise NotImplementedError("use transform_forcefield instead")
return ret_val
#--------------------------------------------------------------------------------#
elif isinstance(to_representation, NormalRepresentation):
B = to_representation.b_matrix
ret_val[...] = tensor.linearly_transformed(B)
return ret_val
else:
raise NotImplementedError(
"Transformation of arbitrary tensors from representation of type '{}' to "
" representations of type '{}' is not implemented.".format(
self.__class__.__name__,
to_representation.__class__.__name__))
def iter_atom_coords(self, with_atom=False):
for i, (a, b, c) in enumerate(grouper(3, self)):
if with_atom:
yield a, b, c, self.atoms[i]
else:
yield a, b, c
def transform_tensor(self, tensor, to_representation):
"""
"""
self.freeze()
to_representation.freeze()
shape = (len(to_representation),) * len(tensor.shape)
ret_val = RepresentationDependentTensor(shape=shape, representation=to_representation)
# --------------------------------------------------------------------------------#
if isinstance(to_representation, CartesianRepresentation):
if len(self) != len(to_representation):
raise ValueError(
"incompatible representation sizes ({} != {})".format(len(self), len(to_representation))
)
if tensor.representation is not self:
raise ValueError(
"representation {} can only transform a tensor whose representation attribute is the same "
" as itself (tensor.representation was {} ".format(self, tensor.representation)
)
if self.molecule.is_linear():
raise NotImplementedError(
"linear molecule cartesian-to-cartesian transformation is not"
" yet implemented. Shouldn't be too hard..."
)
if sanity_checking_enabled:
# TODO use a recentered version of the 'from' representation if it is not centered
pass
# Make sure things are centered
# if not self.molecule.is_centered(cartesian_representation=self):
# raise ValueError("CartesianRepresentation objects transformed from and to"
# " must be centered at the center of mass ({} was not)".format(
# self
# ))
# elif not self.molecule.is_centered(cartesian_representation=to_representation):
# raise ValueError("CartesianRepresentation objects transformed from and to"
# " must be centered at the center of mass ({} was not)".format(
# to_representation
# ))
# ----------------------------------------#
old = self.value
new = to_representation.value
unitconv = self.units.to(to_representation.units)
oldmat = Matrix(old).reshape((len(self) / 3, 3))
newmat = Matrix(new).reshape((len(self) / 3, 3))
# ----------------------------------------#
# Check to see if the molecule is planar. If so, append the cross product of any
# two atoms' positions not colinear with the origin
if any(
norm(oldmat[:dir].view(Vector)) < 1e-12 or norm(newmat[:dir].view(Vector)) < 1e-12 for dir in [X, Y, Z]
):
first_atom = None
# TODO unit concious cutoff (e.g. this would fail if the units of the position matrix were meters)
nonorigin_cutoff = 1e-2
for i, v in enumerate(grouper(3, old)):
if norm(LightVector(v)) > nonorigin_cutoff:
first_atom = i
break
second_atom = None
for i in range(first_atom + 1, len(self) // 3):
# TODO make sure this works with Molecule.linear_cutoff
if (
norm(oldmat[i]) > nonorigin_cutoff
and norm(newmat[i]) > nonorigin_cutoff
and abs(angle_between_vectors(oldmat[first_atom], oldmat[i])) > 1e-5
and abs(angle_between_vectors(newmat[first_atom], newmat[i])) > 1e-5
):
second_atom = i
break
oldmat = Matrix(list(oldmat.rows) + [cross(oldmat[first_atom], oldmat[second_atom])])
newmat = Matrix(list(newmat.rows) + [cross(newmat[first_atom], newmat[second_atom])])
# ----------------------------------------#
rot_mat = newmat.T * np.linalg.pinv(oldmat.T)
# Divide by unit conversion because the representation dependent tensor
# is [some units] *per* representation units
rot_mat /= unitconv
trans_mat = Matrix(shape=(len(self), len(self)))
for idx in xrange(len(self) // 3):
off = idx * 3
trans_mat[off : off + 3, off : off + 3] = rot_mat
order = len(tensor.shape)
# This is basically impossible to read what I'm doing here, but work it out
# looking at the numpy.einsum documentation and you'll see that this is correct.
# Basically, we want to contract the row indices of the transformation matrix
# with each axis of the tensor to be transformed.
einsumargs = sum(([trans_mat, [2 * i, 2 * i + 1]] for i in xrange(order)), [])
einsumargs += [tensor, [i for i in xrange(2 * order) if i % 2 != 0]]
einsumargs += [[i for i in xrange(2 * order) if i % 2 == 0]]
np.einsum(*einsumargs, out=ret_val)
return ret_val
# --------------------------------------------------------------------------------#
elif isinstance(to_representation, InternalRepresentation):
if len(tensor.shape) == 1:
A = to_representation.a_matrix
ret_val[...] = A.T * tensor.view(Vector)
else:
raise NotImplementedError("use transform_forcefield instead")
return ret_val
# --------------------------------------------------------------------------------#
elif isinstance(to_representation, NormalRepresentation):
B = to_representation.b_matrix
ret_val[...] = tensor.linearly_transformed(B)
return ret_val
else:
raise NotImplementedError(
"Transformation of arbitrary tensors from representation of type '{}' to "
" representations of type '{}' is not implemented.".format(
self.__class__.__name__, to_representation.__class__.__name__
)
)
