def tune_geometry(graph, mol):
"""Fine tune a molecular geometry, starting from a (very) poor guess of
the initial geometry.
Do not expect all details to be in perfect condition. A subsequent
optimization with a more accurate level of theory is at least advisable.
Arguments:
graph -- The molecular graph of the system
mol -- The initial guess of the coordinates
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, NewtonGLineSearch
ff = ToyFF(graph)
x_init = mol.coordinates.ravel()
# level 3 geometry optimization: bond lengths + pauli
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-3, 1e-3, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-6, 1e-6, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N,3)))
return mol
def convert_molecule_to_molmod(molecule):
molModMolecule = Molecule(molecule.numbers,
molecule.coordinates,
molecule.label,
symbols=molecule.symbols)
molModMolecule.set_default_graph()
return molModMolecule
def endElement(self, name):
#print "END", name
if name == 'molecule':
if len(self.current_numbers) > 0:
self.current_coordinates = np.array(self.current_coordinates)*angstrom
molecule = Molecule(self.current_numbers, self.current_coordinates, self.current_title)
molecule.extra = self.current_extra
molecule.atoms_extra = self.current_atoms_extra
name_to_index = {}
for counter, name in enumerate(self.current_atom_names):
name_to_index[name] = counter
edges = set()
current_bonds_extra = {}
for name1, name2, extra in self.current_bonds:
i1 = name_to_index.get(name1)
i2 = name_to_index.get(name2)
if i1 is not None and i2 is not None:
edge = frozenset([i1, i2])
if len(extra) > 0:
current_bonds_extra[edge] = extra
edges.add(edge)
molecule.bonds_extra = current_bonds_extra
if len(edges) == 0:
molecule.graph = None
else:
molecule.graph = MolecularGraph(edges, self.current_numbers)
del self.current_atom_names
del self.current_bonds
self.molecules.append(molecule)
self.current_title = None
def load_pdb(filename):
"""
Loads a single molecule from a pdb file.
This function does support only a small fragment from the pdb specification.
It assumes that there is only one molecular geometry in the pdb file.
"""
f = file(filename)
numbers = []
coordinates = []
occupancies = []
betas = []
for line in f:
if line.startswith("ATOM"):
symbol = line[76:78].strip()
numbers.append(periodic[symbol].number)
coordinates.append(
[float(line[30:38]),
float(line[38:46]),
float(line[46:54])])
occupancies.append(float(line[54:60]))
betas.append(float(line[60:66]))
f.close()
if len(numbers) > 0:
molecule = Molecule(numbers, coordinates)
molecule.occupancies = numpy.array(occupancies)
molecule.betas = numpy.array(betas)
return molecule
else:
raise IOError("No molecule found in pdb file %s" % filename)
def get_molecule(self, index=0):
"""Get a molecule from the trajectory
Optional argument:
| ``index`` -- The frame index [default=0]
"""
return Molecule(self.numbers, self.geometries[index], self.titles[index], symbols=self.symbols)
def test_one(xyz_fn, checks, reorder=False):
mol = XYZFile(xyz_fn).get_molecule()
graph = MolecularGraph.from_geometry(mol)
zmat_gen = ZMatrixGenerator(graph)
if reorder is False:
self.assertArraysEqual(zmat_gen.new_index, numpy.arange(mol.size))
self.assertArraysEqual(zmat_gen.old_index, numpy.arange(mol.size))
zmat0 = zmat_gen.cart_to_zmat(mol.coordinates)
for field, index, value in checks:
self.assertAlmostEqual(zmat0[field][index], value, 2, "%s:%i %f!=%f" % (field,index,zmat0[field][index],value))
numbers0, coordinates0 = zmat_to_cart(zmat0)
mol0 = Molecule(numbers0, coordinates0)
mol0.write_to_file("output/zmat_%s" % os.path.basename(xyz_fn))
graph0 = MolecularGraph.from_geometry(mol0)
zmat_gen0 = ZMatrixGenerator(graph0)
self.assertArraysEqual(zmat_gen0.new_index, numpy.arange(mol.size))
self.assertArraysEqual(zmat_gen0.old_index, numpy.arange(mol.size))
zmat1 = zmat_gen0.cart_to_zmat(mol0.coordinates)
for field, index, value in checks:
self.assertAlmostEqual(zmat1[field][index], value, 2, "%s:%i %f!=%f" % (field,index,zmat1[field][index],value))
numbers1, coordinates1 = zmat_to_cart(zmat1)
self.assertArraysEqual(numbers0, numbers1)
self.assertArraysAlmostEqual(coordinates0, coordinates1, 1e-5)
def get_optimized_molecule(self):
opt_coor = self.get_optimization_coordinates()
if len(opt_coor) == 0:
return None
else:
return Molecule(
self.molecule.numbers,
opt_coor[-1],
)
def get_first_molecule(self):
"""Get the first molecule from the trajectory
This can be useful to configure your program before handeling the
actual trajectory.
"""
title, coordinates = self._first
molecule = Molecule(self.numbers, coordinates, title, symbols=self.symbols)
return molecule
def get_first_molecule(self):
if self._first is None:
raise Error(
"get_first_molecule must be called before the first iteration."
)
else:
title, coordinates = self._first
molecule = Molecule(self.numbers, coordinates, title)
return molecule
def get_optimized_molecule(self):
"""Return a molecule object of the optimal geometry"""
opt_coor = self.get_optimization_coordinates()
if len(opt_coor) == 0:
return None
else:
return Molecule(
self.molecule.numbers,
opt_coor[-1],
)
def _analyze(self):
if ("Atomic numbers"
in self.fields) and ("Current cartesian coordinates"
in self.fields):
self.molecule = Molecule(
self.fields["Atomic numbers"],
numpy.reshape(self.fields["Current cartesian coordinates"],
(-1, 3)),
self.title,
)
def _analyze(self):
"""Convert a few elementary fields into a molecule object"""
if ("Atomic numbers"
in self.fields) and ("Current cartesian coordinates"
in self.fields):
self.molecule = Molecule(
self.fields["Atomic numbers"],
np.reshape(self.fields["Current cartesian coordinates"],
(-1, 3)),
self.title,
)
def guess_geometry(graph, unitcell_active, unitcell, unitcell_reciproke):
"""Construct a molecular geometry based on a molecular graph.
This routine does not require initial coordinates and will give a very
rough picture of the initial geometry. Do not expect all details to be
in perfect condition. A subsequent optimization with a more accurate
level of theory is at least advisable.
Arguments:
graph -- The molecular graph of the system
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, NewtonGLineSearch
ff = ToyFF(graph, unitcell_active, unitcell, unitcell_reciproke)
x_init = numpy.random.normal(0,1,N*3)
# level 1 geometry optimization: graph based
ff.dm_quad = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-10, 1e-8, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
# level 2 geometry optimization: graph based + pauli repulsion
ff.dm_quad = 1.0
ff.dm_reci = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-10, 1e-8, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
# Add a little noise to avoid saddle points
x_init += numpy.random.uniform(-0.01, 0.01, len(x_init))
# level 3 geometry optimization: bond lengths + pauli
ff.dm_quad = 0.0
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-3, 1e-3, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init, ff, NewtonGLineSearch, 1e-6, 1e-6, 2*N, 500, 50, do_gradient=True, verbose=False)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N,3)))
return mol
def read_cube_header(f):
# skip the first two lines
title = f.readline().strip()
subtitle = f.readline().strip()
def read_grid_line(line):
"""Read a grid line from the cube file"""
words = line.split()
return (int(words[0]),
np.array([float(words[1]),
float(words[2]),
float(words[3])], float)
# all coordinates in a cube file are in atomic units
)
# number of atoms and origin of the grid
natom, origin = read_grid_line(f.readline())
# numer of grid points in A direction and step vector A, and so on
nrep0, axis0 = read_grid_line(f.readline())
nrep1, axis1 = read_grid_line(f.readline())
nrep2, axis2 = read_grid_line(f.readline())
nrep = np.array([nrep0, nrep1, nrep2], int)
axes = np.array([axis0, axis1, axis2])
def read_coordinate_line(line):
"""Read an atom number and coordinate from the cube file"""
words = line.split()
return (int(words[0]), float(words[1]),
np.array([float(words[2]),
float(words[3]),
float(words[4])], float)
# all coordinates in a cube file are in atomic units
)
numbers = np.zeros(natom, int)
nuclear_charges = np.zeros(natom, float)
coordinates = np.zeros((natom, 3), float)
for i in range(natom):
numbers[i], nuclear_charges[i], coordinates[i] = read_coordinate_line(
f.readline())
molecule = Molecule(numbers, coordinates, title=title)
return molecule, origin, axes, nrep, subtitle, nuclear_charges
def get_molecule(self, index=0):
return Molecule(self.numbers, self.geometries[index],
self.titles[index])
def __next__(self):
"""Load the next molecule from the SDF file
This method is part of the iterator protocol.
"""
while True:
title = next(self.f)
if len(title) == 0:
raise StopIteration
else:
title = title.strip()
next(self.f) # skip line
next(self.f) # skip empty line
words = next(self.f).split()
if len(words) < 2:
raise FileFormatError(
"Expecting at least two numbers at fourth line.")
try:
num_atoms = int(words[0])
num_bonds = int(words[1])
except ValueError:
raise FileFormatError(
"Expecting at least two numbers at fourth line.")
numbers = np.zeros(num_atoms, int)
coordinates = np.zeros((num_atoms, 3), float)
for i in range(num_atoms):
words = next(self.f).split()
if len(words) < 4:
raise FileFormatError(
"Expecting at least four words on an atom line.")
try:
coordinates[i, 0] = float(words[0])
coordinates[i, 1] = float(words[1])
coordinates[i, 2] = float(words[2])
except ValueError:
raise FileFormatError(
"Coordinates must be floating point numbers.")
atom = periodic[words[3]]
if atom is None:
raise FileFormatError("Unrecognized atom symbol: %s" %
words[3])
numbers[i] = atom.number
coordinates *= angstrom
edges = []
orders = np.zeros(num_bonds, int)
for i in range(num_bonds):
words = next(self.f).split()
if len(words) < 3:
raise FileFormatError(
"Expecting at least three numbers on a bond line.")
try:
edges.append((int(words[0]) - 1, int(words[1]) - 1))
orders[i] = int(words[2])
except ValueError:
raise FileFormatError(
"Expecting at least three numbers on a bond line.")
formal_charges = np.zeros(len(numbers), int)
line = next(self.f)
while line != "M END\n":
if line.startswith("M CHG"):
words = line[6:].split(
)[1:] # drop the first number which is the number of charges
i = 0
while i < len(words) - 1:
try:
formal_charges[int(words[i]) - 1] = int(words[i +
1])
except ValueError:
raise FileFormatError(
"Expecting only integer formal charges.")
i += 2
line = next(self.f)
# Read on to the next molecule
for line in self.f:
if line == "$$$$\n":
break
molecule = Molecule(numbers, coordinates, title)
molecule.formal_charges = formal_charges
molecule.formal_charges.setflags(write=False)
molecule.graph = MolecularGraph(edges, numbers, orders)
return molecule
def tune_geometry(graph, mol, unit_cell=None, verbose=False):
"""Fine tune a molecular geometry, starting from a (very) poor guess of
the initial geometry.
Do not expect all details to be in perfect condition. A subsequent
optimization with a more accurate level of theory is at least advisable.
Arguments:
| ``graph`` -- The molecular graph of the system, see
:class:molmod.molecular_graphs.MolecularGraph
| ``mol`` -- A :class:molmod.molecules.Molecule class with the initial
guess of the coordinates
Optional argument:
| ``unit_cell`` -- periodic boundry conditions, see
:class:`molmod.unit_cells.UnitCell`
| ``verbose`` -- Show optimizer progress when True
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, ConjugateGradient, \
NewtonLineSearch, ConvergenceCondition, StopLossCondition
search_direction = ConjugateGradient()
line_search = NewtonLineSearch()
convergence = ConvergenceCondition(grad_rms=1e-6, step_rms=1e-6)
stop_loss = StopLossCondition(max_iter=500, fun_margin=1.0)
ff = ToyFF(graph, unit_cell)
x_init = mol.coordinates.ravel()
# level 3 geometry optimization: bond lengths + pauli
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N, 3)))
return mol
def stop_collecting(self):
self.molecules.append(Molecule(self.current_atoms))
del self.current_atoms
def random_dimer(molecule0, molecule1, thresholds, shoot_max):
"""Create a random dimer.
molecule0 and molecule1 are placed in one reference frame at random
relative positions. Interatomic distances are above the thresholds.
Initially a dimer is created where one interatomic distance approximates
the threshold value. Then the molecules are given an additional
separation in the range [0, shoot_max].
thresholds has the following format:
{frozenset([atom_number1, atom_number2]): distance}
"""
# apply a random rotation to molecule1
center = np.zeros(3, float)
angle = np.random.uniform(0, 2 * np.pi)
axis = random_unit()
rotation = Complete.about_axis(center, angle, axis)
cor1 = np.dot(molecule1.coordinates, rotation.r)
# select a random atom in each molecule
atom0 = np.random.randint(len(molecule0.numbers))
atom1 = np.random.randint(len(molecule1.numbers))
# define a translation of molecule1 that brings both atoms in overlap
delta = molecule0.coordinates[atom0] - cor1[atom1]
cor1 += delta
# define a random direction
direction = random_unit()
cor1 += 1 * direction
# move molecule1 along this direction until all intermolecular atomic
# distances are above the threshold values
threshold_mat = np.zeros((len(molecule0.numbers), len(molecule1.numbers)),
float)
distance_mat = np.zeros((len(molecule0.numbers), len(molecule1.numbers)),
float)
for i1, n1 in enumerate(molecule0.numbers):
for i2, n2 in enumerate(molecule1.numbers):
threshold = thresholds.get(frozenset([n1, n2]))
threshold_mat[i1, i2] = threshold**2
while True:
cor1 += 0.1 * direction
distance_mat[:] = 0
for i in 0, 1, 2:
distance_mat += np.subtract.outer(molecule0.coordinates[:, i],
cor1[:, i])**2
if (distance_mat > threshold_mat).all():
break
# translate over a random distance [0, shoot] along the same direction
# (if necessary repeat until no overlap is found)
while True:
cor1 += direction * np.random.uniform(0, shoot_max)
distance_mat[:] = 0
for i in 0, 1, 2:
distance_mat += np.subtract.outer(molecule0.coordinates[:, i],
cor1[:, i])**2
if (distance_mat > threshold_mat).all():
break
# done
dimer = Molecule(np.concatenate([molecule0.numbers, molecule1.numbers]),
np.concatenate([molecule0.coordinates, cor1]))
dimer.direction = direction
dimer.atom0 = atom0
dimer.atom1 = atom1
return dimer
def guess_geometry(graph, unit_cell=None, verbose=False):
"""Construct a molecular geometry based on a molecular graph.
This routine does not require initial coordinates and will give a very
rough picture of the initial geometry. Do not expect all details to be
in perfect condition. A subsequent optimization with a more accurate
level of theory is at least advisable.
Argument:
| ``graph`` -- The molecular graph of the system, see
:class:molmod.molecular_graphs.MolecularGraph
Optional argument:
| ``unit_cell`` -- periodic boundry conditions, see
:class:`molmod.unit_cells.UnitCell`
| ``verbose`` -- Show optimizer progress when True
"""
N = len(graph.numbers)
from molmod.minimizer import Minimizer, ConjugateGradient, \
NewtonLineSearch, ConvergenceCondition, StopLossCondition
search_direction = ConjugateGradient()
line_search = NewtonLineSearch()
convergence = ConvergenceCondition(grad_rms=1e-6, step_rms=1e-6)
stop_loss = StopLossCondition(max_iter=500, fun_margin=0.1)
ff = ToyFF(graph, unit_cell)
x_init = np.random.normal(0, 1, N * 3)
# level 1 geometry optimization: graph based
ff.dm_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 2 geometry optimization: graph based + pauli repulsion
ff.dm_quad = 1.0
ff.dm_reci = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# Add a little noise to avoid saddle points
x_init += np.random.uniform(-0.01, 0.01, len(x_init))
# level 3 geometry optimization: bond lengths + pauli
ff.dm_quad = 0.0
ff.dm_reci = 0.2
ff.bond_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
# level 4 geometry optimization: bond lengths + bending angles + pauli
ff.bond_quad = 0.0
ff.bond_hyper = 1.0
ff.span_quad = 1.0
minimizer = Minimizer(x_init,
ff,
search_direction,
line_search,
convergence,
stop_loss,
anagrad=True,
verbose=verbose)
x_init = minimizer.x
x_opt = x_init
mol = Molecule(graph.numbers, x_opt.reshape((N, 3)))
return mol
